The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements similar to neurons. The key aspects covered are:
- Neural networks can perform tasks that are difficult for traditional algorithms, such as pattern recognition.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections and can adapt based on training data.
- Training involves presenting examples to the network and adjusting the weighted connections between nodes until the network outputs the desired targets.
- Once trained, a neural network can be used to analyze new input data in a similar way to the brain.
1. The document discusses several key aspects of artificial neural networks including their architecture, learning algorithms, and applications.
2. ANNs are modeled after biological neural networks and utilize features such as parallel distributed processing, learning from examples, and the ability to generalize.
3. The document covers various ANN architectures including feedforward networks, recurrent networks, and different learning methods like supervised and unsupervised learning.
The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are modeled after biological neural networks and neurons. The key concepts covered include the basic structure and functioning of artificial neurons, different types of learning in ANNs, commonly used network architectures, and applications of ANNs. Examples of applications discussed are classification, recognition, assessment, forecasting and prediction. The document also notes how ANNs are used across various fields including computer science, statistics, engineering, cognitive science, neurophysiology, physics and biology.
Neural networks of artificial intelligencealldesign
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
Neural network and artificial intelligentHapPy SumOn
This document discusses neural networks and artificial intelligence. It defines artificial intelligence as machines programmed to think like humans, and neural networks as computational models inspired by the human brain. The document explains that neural networks are used in artificial intelligence to help machines solve complex problems. It then provides details on the basic structure and learning mechanisms of neural networks, describing how networks are composed of interconnected neurons that can learn from examples to perform tasks like pattern recognition.
1. The document discusses several key aspects of artificial neural networks including their architecture, learning algorithms, and applications.
2. ANNs are modeled after biological neural networks and utilize features such as parallel distributed processing, learning from examples, and the ability to generalize.
3. The document covers various ANN architectures including feedforward networks, recurrent networks, and different learning methods like supervised and unsupervised learning.
The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are modeled after biological neural networks and neurons. The key concepts covered include the basic structure and functioning of artificial neurons, different types of learning in ANNs, commonly used network architectures, and applications of ANNs. Examples of applications discussed are classification, recognition, assessment, forecasting and prediction. The document also notes how ANNs are used across various fields including computer science, statistics, engineering, cognitive science, neurophysiology, physics and biology.
Neural networks of artificial intelligencealldesign
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
Neural network and artificial intelligentHapPy SumOn
This document discusses neural networks and artificial intelligence. It defines artificial intelligence as machines programmed to think like humans, and neural networks as computational models inspired by the human brain. The document explains that neural networks are used in artificial intelligence to help machines solve complex problems. It then provides details on the basic structure and learning mechanisms of neural networks, describing how networks are composed of interconnected neurons that can learn from examples to perform tasks like pattern recognition.
This document outlines advances in deep learning and neural networks. It discusses challenges in machine learning like feature extraction. It describes how neuroscience experiments showed the brain's ability to learn new tasks. Neural networks aim to mimic the brain through techniques like backpropagation to train multi-layer models. Breakthroughs like pre-training and convolutional networks helped scale networks to many layers. Deep learning is now used in speech translation, image recognition, handwriting recognition and more.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
This document summarizes artificial neural networks. It discusses how neural networks are composed of interconnected neurons that can learn complex behaviors through simple principles. Neural networks can be used for applications like pattern recognition, noise reduction, and prediction. The key components of neural networks are neurons, synapses, weights, thresholds, and activation functions. Neural networks offer advantages like adaptability and fault tolerance, though they are not exact and can be complex. Examples of neural network applications discussed include object trajectory learning, radiosity for virtual reality, speechreading, target detection and tracking, and robotics.
Neural networks are inspired by biological neural systems. An artificial neural network (ANN) is an information processing paradigm that is modeled after the human brain. ANNs learn by example, through a learning process, like the way synapses strengthen in the human brain. An ANN is composed of interconnected processing nodes that work together to solve problems. It can be trained to perform tasks by considering examples without being explicitly programmed.
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
This document provides an introduction to artificial neural networks. It describes how neural networks are inspired by and similar to the human brain, yet take a different approach to problem solving than conventional computers. The document outlines various types of neural network architectures and applications of neural networks in areas like medicine, business, and pattern recognition. It also provides historical background on the development of neural networks and compares their abilities to conventional algorithms.
This presentation educates you about Neural Network, How artificial neural networks work?, How neural networks learn?, Types of Neural Networks, Advantages and Disadvantages of artificial neural networks and Applications of artificial neural networks.
For more topics stay tuned with Learnbay.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Thanks to Deep Learning, Artificial Intelligence is now getting smart. Deep Learning models attempt to mimic the activity of the neocortex. It is understood that the activity of these layers of neurons is what constitutes a brain to be able to "think". These models learn to recognize patterns in digital representations of data in a very similar sense to humans. In this survey report, we introduce the most important concepts of Deep Learning along with the state of the art models that are now widely adopted in commercial products.
Neural networks are modeled after the human brain and are made up of interconnected nodes that mimic neurons. Machine learning uses neural networks to find patterns in data and make predictions. Recent advances in hardware have enabled more powerful neural networks for applications like image recognition, medical diagnosis, business marketing and user interfaces. However, neural networks require large datasets for training and can become unstable on larger problems. Future applications may include using neural networks in consumer products to aid decision making.
Artificial neural network is the branch of artificial intelligence. Definition word by word with examples, short history of neural network, what is neuron, why neural network needed, human brain neural network, BRAIN vs ANN,
This document provides a summary of a study on deep learning. It introduces artificial neural networks as the building blocks of deep learning architectures. Neural networks are modeled after the human brain and consist of interconnected nodes that learn patterns in data. Deep learning aims to develop human-level artificial intelligence. The document explains key concepts like activation functions, which introduce non-linearity, and backpropagation, which is used to train neural networks by minimizing error. It surveys popular deep learning models and their objectives, like convolutional neural networks for computer vision and recurrent neural networks for language.
Neural networks are computing systems inspired by biological neural networks. They are composed of interconnected nodes that process input data and transmit signals to each other. The document discusses various types of neural networks including feedforward, recurrent, convolutional, and modular neural networks. It also describes the basic architecture of neural networks including input, hidden, and output layers. Neural networks can be used for applications like pattern recognition, data classification, and more. They are well-suited for complex, nonlinear problems. The document provides an overview of neural networks and their functioning.
Artificial neural networks and its applications PoojaKoshti2
This presentation provides an overview of artificial neural networks (ANN), including what they are, how they work, different types, and applications. It defines ANN as biologically inspired simulations used for tasks like clustering, classification, and pattern recognition. The presentation explains that ANN learn by processing information in parallel through nodes and weighted connections, similar to the human brain. It also outlines various ANN architectures, such as perceptrons, recurrent networks, and convolutional networks. Finally, the presentation discusses common applications of ANN in domains like process control, medical diagnosis, and targeted marketing.
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in RManish Saraswat
Simple guide which explains deep learning and neural network with hands on experience in R using MXnet and H2o package. It also explains gradient descent and backpropagation algorithm.
Complete tutorial: http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-r
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
This document provides an overview of artificial neural networks and their application as a model of the human brain. It discusses the biological neuron, different types of neural networks including feedforward, feedback, time delay, and recurrent networks. It also covers topics like learning in perceptrons, training algorithms, applications of neural networks, and references key concepts like connectionism, associative memory, and massive parallelism in the brain.
A short presentation that I made for a philosophy of mind course taken through the Continuing Education Department at Oxford University. This presentation explores the concept of Extended Mind in Artificial Intelligence through an examination of machine learning and neural networks.
Artificial neural network based chinese medicine diagnosis in decision suppor...Dr. Wilfred Lin (Ph.D.)
HerbMiners Informatics Limited is a clinical Traditional Chinese Medicine (TCM) intelligence software solutions company. HerbMiners Informatics Limited focuses on research in TCM data mining, which aims to reveal relationships between symptoms, illnesses, herbs and prescriptions. HerbMiners Informatics Limited also provides artificial intelligence software solutions which assist hospitals and clinics for TCM modernization and patient records digitization.
This document outlines advances in deep learning and neural networks. It discusses challenges in machine learning like feature extraction. It describes how neuroscience experiments showed the brain's ability to learn new tasks. Neural networks aim to mimic the brain through techniques like backpropagation to train multi-layer models. Breakthroughs like pre-training and convolutional networks helped scale networks to many layers. Deep learning is now used in speech translation, image recognition, handwriting recognition and more.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
This document summarizes artificial neural networks. It discusses how neural networks are composed of interconnected neurons that can learn complex behaviors through simple principles. Neural networks can be used for applications like pattern recognition, noise reduction, and prediction. The key components of neural networks are neurons, synapses, weights, thresholds, and activation functions. Neural networks offer advantages like adaptability and fault tolerance, though they are not exact and can be complex. Examples of neural network applications discussed include object trajectory learning, radiosity for virtual reality, speechreading, target detection and tracking, and robotics.
Neural networks are inspired by biological neural systems. An artificial neural network (ANN) is an information processing paradigm that is modeled after the human brain. ANNs learn by example, through a learning process, like the way synapses strengthen in the human brain. An ANN is composed of interconnected processing nodes that work together to solve problems. It can be trained to perform tasks by considering examples without being explicitly programmed.
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
This document provides an introduction to artificial neural networks. It describes how neural networks are inspired by and similar to the human brain, yet take a different approach to problem solving than conventional computers. The document outlines various types of neural network architectures and applications of neural networks in areas like medicine, business, and pattern recognition. It also provides historical background on the development of neural networks and compares their abilities to conventional algorithms.
This presentation educates you about Neural Network, How artificial neural networks work?, How neural networks learn?, Types of Neural Networks, Advantages and Disadvantages of artificial neural networks and Applications of artificial neural networks.
For more topics stay tuned with Learnbay.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Thanks to Deep Learning, Artificial Intelligence is now getting smart. Deep Learning models attempt to mimic the activity of the neocortex. It is understood that the activity of these layers of neurons is what constitutes a brain to be able to "think". These models learn to recognize patterns in digital representations of data in a very similar sense to humans. In this survey report, we introduce the most important concepts of Deep Learning along with the state of the art models that are now widely adopted in commercial products.
Neural networks are modeled after the human brain and are made up of interconnected nodes that mimic neurons. Machine learning uses neural networks to find patterns in data and make predictions. Recent advances in hardware have enabled more powerful neural networks for applications like image recognition, medical diagnosis, business marketing and user interfaces. However, neural networks require large datasets for training and can become unstable on larger problems. Future applications may include using neural networks in consumer products to aid decision making.
Artificial neural network is the branch of artificial intelligence. Definition word by word with examples, short history of neural network, what is neuron, why neural network needed, human brain neural network, BRAIN vs ANN,
This document provides a summary of a study on deep learning. It introduces artificial neural networks as the building blocks of deep learning architectures. Neural networks are modeled after the human brain and consist of interconnected nodes that learn patterns in data. Deep learning aims to develop human-level artificial intelligence. The document explains key concepts like activation functions, which introduce non-linearity, and backpropagation, which is used to train neural networks by minimizing error. It surveys popular deep learning models and their objectives, like convolutional neural networks for computer vision and recurrent neural networks for language.
Neural networks are computing systems inspired by biological neural networks. They are composed of interconnected nodes that process input data and transmit signals to each other. The document discusses various types of neural networks including feedforward, recurrent, convolutional, and modular neural networks. It also describes the basic architecture of neural networks including input, hidden, and output layers. Neural networks can be used for applications like pattern recognition, data classification, and more. They are well-suited for complex, nonlinear problems. The document provides an overview of neural networks and their functioning.
Artificial neural networks and its applications PoojaKoshti2
This presentation provides an overview of artificial neural networks (ANN), including what they are, how they work, different types, and applications. It defines ANN as biologically inspired simulations used for tasks like clustering, classification, and pattern recognition. The presentation explains that ANN learn by processing information in parallel through nodes and weighted connections, similar to the human brain. It also outlines various ANN architectures, such as perceptrons, recurrent networks, and convolutional networks. Finally, the presentation discusses common applications of ANN in domains like process control, medical diagnosis, and targeted marketing.
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in RManish Saraswat
Simple guide which explains deep learning and neural network with hands on experience in R using MXnet and H2o package. It also explains gradient descent and backpropagation algorithm.
Complete tutorial: http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-r
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
This document provides an overview of artificial neural networks and their application as a model of the human brain. It discusses the biological neuron, different types of neural networks including feedforward, feedback, time delay, and recurrent networks. It also covers topics like learning in perceptrons, training algorithms, applications of neural networks, and references key concepts like connectionism, associative memory, and massive parallelism in the brain.
A short presentation that I made for a philosophy of mind course taken through the Continuing Education Department at Oxford University. This presentation explores the concept of Extended Mind in Artificial Intelligence through an examination of machine learning and neural networks.
Artificial neural network based chinese medicine diagnosis in decision suppor...Dr. Wilfred Lin (Ph.D.)
HerbMiners Informatics Limited is a clinical Traditional Chinese Medicine (TCM) intelligence software solutions company. HerbMiners Informatics Limited focuses on research in TCM data mining, which aims to reveal relationships between symptoms, illnesses, herbs and prescriptions. HerbMiners Informatics Limited also provides artificial intelligence software solutions which assist hospitals and clinics for TCM modernization and patient records digitization.
Artificial neural network based cancer cell classificationAlexander Decker
This document summarizes an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical images. The system performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification using a neural network ensemble. Segmentation detects threshold points using Harris corner detection and performs region growing from these seed points. Feature extraction converts the image data into numerical form using Tamura texture features that capture variations in illumination and surfaces that human vision and surgeons use to differentiate cancerous and non-cancerous cells. The neural network is trained on a large set of labeled data to accurately classify cells.
An intelligent agent is anything that can perceive its environment through sensors and act upon that environment through effectors. Intelligent agents include humans, robots, and thermostats. An agent's behavior is determined by its agent function, which maps percept sequences to actions. Rational agents are those that maximize their performance as defined by a performance measure. Agent programs implement agent functions in a way that uses minimal code rather than exhaustive lookup tables. There are different types of agent programs including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
Artificial intelligence is the study and creation of intelligent machines that can think and act like humans. Cognitive science is an interdisciplinary field that studies intelligence and behavior in natural and artificial systems. The goal of artificial intelligence is to create machines that can think and act rationally.
Artificial Intelligence (AI) is essential to provide value added Internet of Things (IoT) services by finding the patterns, correlations and anomalies in user behaviors for autonomous context-aware actions of the IoT system surrounding the user. Patents can provide insights regarding the state of the art and technical details of the AI innovation for the IoT applications.
This document presents a method for detecting natural gas pipeline leaks using a binary matrix analyzer and neural network. The method involves gathering image data of pipelines, extracting binary data from the images using a matrix analyzer, inputting the binary data into the Matlab neural network toolbox to train and test an artificial neural network model. The trained neural network was able to detect pipeline leaks with 98.52% accuracy based on simulations. The method provides an intelligent system for automating natural gas pipeline leak detection as a safer and more cost-effective alternative to traditional inspection methods.
ARTIFICIAL NEURAL NETWORK FOR DIAGNOSIS OF PANCREATIC CANCERIJCI JOURNAL
Cancer is malignant growth or tumour which forms due to an uncontrolled division of cells in a part of
body which may even lead to death. These are of different types depending upon the part of body affected.
If it is Pancreas then the disease is termed as Pancreatic Cancer. This paper presents an Artificial Neural
Network model to diagnose pancreatic cancer based on a set of symptoms. An ANN model is created after
analysing the actual procedure of disease diagnosis by the doctor. An approach to detect various stages of
cancer affected in pancreas is presented in the paper. Results of the study suggest the advantage of using
ANN model instead of manual disease diagnosis.
Neural networks in accounting and auditing slidecastm13chan
This document provides an overview of neural networks and their potential applications in accounting and auditing. It discusses how neural networks work, their history of use since the 1990s, and current applications in areas like continuous auditing, fraud detection, and improving auditor decisions. While neural networks have seen limited adoption in accounting and auditing so far, the document argues they could benefit the field by identifying patterns in large datasets that humans may miss. It recommends auditing professionals implement neural network models with a full-time commitment to help direct their work.
Neural network based energy efficient clustering and routingambitlick
This document summarizes a paper that proposes a neural network based approach for energy efficient clustering and routing in wireless sensor networks. The key points are:
1. It proposes a neural network based clustering algorithm to select cluster heads in a way that balances energy consumption.
2. It defines a routing metric based on transmission and reception energy and uses it to formulate the routing problem as a linear program to optimize energy efficiency.
3. It presents algorithms for cluster head selection using the neural network, and for multi-path routing and data transmission based on the routing metric and linear program formulation.
The document discusses several types of artificial neural network architectures:
- The Perceptron network classifies inputs into categories by adjusting weights between input and output units.
- The Adaline network receives multiple inputs and one bias input, with weights that are positive or negative. It compares actual and predicted outputs.
- The Madaline network contains input, Adaline, and output layers. It is used in communication systems for equalization and noise cancellation.
- The Backpropagation network is a multilayer feedforward network that calculates outputs from inputs and uses backward signals in learning.
- The Autoassociative memory network trains inputs and outputs to be the same, connecting input and output layers with weights.
- Maxnet and
This document provides an overview of artificial intelligence and robotics. It discusses the foundations and types of AI, including strong AI and weak AI. It then describes some applications of AI in areas like security, medicine, engineering, and more. Specific examples of AI systems are also summarized, such as telephone translators, chess playing computers, and chatterbots like Watson. The document concludes with a section on robots, describing famous fictional robots like HAL 9000 and real world concepts cars like KITT. It also lists the three laws of robotics.
Este documento presenta un resumen de las redes neuronales y sus aplicaciones en los negocios. Describe tres modelos de redes neuronales, la historia de su evolución a través de cinco etapas, y sus usos en marketing, banca y finanzas. Finalmente, analiza el data mining y ofrece conclusiones sobre este tema.
Neural networks are modeled after the human brain and consist of interconnected nodes that process information using activation functions. They can be trained to recognize patterns in data and make predictions. The network is initialized with random weights and biases then trained via backpropagation to minimize an error function by adjusting the weights. Issues that can arise include overfitting, choosing the number of hidden layers and units, and multiple local minima. Bayesian neural networks place prior distributions over the weights to better model uncertainty. Ensemble methods like bagging and boosting can improve performance.
- Artificial neural networks are inspired by biological neural networks and try to mimic their learning mechanisms by modifying synaptic strengths through an optimization process.
- Learning in neural networks can be formulated as a function approximation task where the network learns to approximate a function by minimizing an error measure through optimization of synaptic weights.
- A single hidden layer neural network is capable of learning nonlinear function approximations if general optimization methods are applied to update the synaptic weights.
Modelling Mobile payment services revenue using Artificial Neural Network Kyalo Richard
This presentation elaborate application of Neural Network in modelling mobile payment services in kenya.The policy implication of this study is that ANN can be used to model revenue from mobile payments services, which is certainly useful for various financial players such as government and policy makers of the country.
This document discusses using an artificial neural network to forecast power loads by taking the University of Lagos as a sample space. It involves gathering and arranging historical load data, determining an appropriate network type and topology, training the network using an algorithm, and analyzing the results to test the network's accuracy in predicting loads. The methodology includes randomizing and tagging the training data, experimenting to determine the network topology, training with cross-validation, and performing sensitivity and mean squared error analysis on the network.
This document discusses artificial intelligence and robotics. It covers:
- AI is entering the third and final stage of technological evolution involving automation and replicating human senses.
- Video games are a major area of experimentation for AI and what happens in this industry should be closely watched.
- Those controlling large data sets, like Google and Facebook, stand to be the likely winners in AI unless new business models are invented, as data is critical for training AI systems.
The document provides an overview of artificial neural networks and biological neural networks. It discusses the components and functions of the human nervous system including the central nervous system made up of the brain and spinal cord, as well as the peripheral nervous system. The four main parts of the brain - cerebrum, cerebellum, diencephalon, and brainstem - are described along with their roles in processing sensory information and controlling bodily functions. A brief history of artificial neural networks is also presented.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
This document provides an overview and summary of a student project report on simulating a feed forward artificial neural network in C++. The report includes an abstract, table of contents, list of figures, and 5 chapters that discuss the objectives of the project, provide background on artificial neural networks, describe the design and implementation of a 3-layer feed forward neural network using backpropagation, present the results, and provide references. The design section explains the backpropagation algorithm and provides pseudocode for calculating outputs at each layer. The implementation section provides pseudocode for training patterns and minimizing error.
Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
Nature Inspired Reasoning Applied in Semantic Webguestecf0af
1) Neural networks are computational structures inspired by biological neural networks and have been successfully used to solve complex tasks like image recognition and natural language processing.
2) Neural networks consist of interconnected nodes that perform simple mathematical functions to produce outputs. The connections between nodes and their weights can be modified through training to solve problems.
3) Nature inspired algorithms like neural networks are well-suited for semantic web problems because they can process large amounts of information quickly to find good enough solutions.
1. Neural networks are inspired by the human brain and are able to perform complex tasks like pattern recognition much faster than conventional computers. They learn by adjusting the strengths of connections between neurons.
2. The document discusses different types of neural network architectures including single-layer feedforward networks, multilayer feedforward networks, and recurrent networks. Multilayer feedforward networks are commonly used and can be trained with backpropagation.
3. Neural networks operate by receiving inputs, performing computations through interconnected nodes that emulate neurons, and producing outputs. Learning involves modifying the weights between nodes to optimize performance on tasks.
This document discusses quantum neural networks. It begins by defining artificial neural networks as interconnected processing elements that process information through dynamic responses to external inputs. The document then provides more details on the basics of neural networks, including their typical layered organization and use of weighted connections and activation functions. It also discusses how neural networks differ from conventional computing by operating in parallel rather than sequentially, and provides some examples of neural network applications and limitations.
The document discusses artificial neural networks (ANNs). It defines ANNs as computational models inspired by biological neural networks. The basic structure and types of ANNs are explained, including feed forward and feedback networks. The document also covers ANN learning methods like supervised, unsupervised, and reinforcement learning. Applications of ANNs span various domains like aerospace, automotive, military, electronics, and more. While ANNs can perform complex tasks, they require extensive training and processing power for large networks.
An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.
Neural networks are algorithms that mimic the human brain in recognizing patterns in vast amounts of data. They can adapt to new inputs without redesign. Neural networks can be biological, composed of real neurons, or artificial, for solving AI problems. Artificial neural networks consist of processing units like neurons that learn from inputs to produce outputs. They are used for applications like classification, pattern recognition, optimization, and more.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and consist of interconnected artificial neurons that process information. The document describes common ANN architectures like multilayer perceptrons and radial basis function networks. It also summarizes different ANN learning paradigms such as supervised, unsupervised, and reinforcement learning. Specific learning rules and algorithms are mentioned, including the perceptron rule, Hebbian learning, competitive learning, and backpropagation. Applications of ANNs discussed include pattern recognition, clustering, prediction, and data compression.
Neural networks are parallel computing devices.docx.pdfneelamsanjeevkumar
Neural networks are parallel computing systems modeled after the human brain that can perform tasks like pattern recognition and data analysis. Artificial neural networks (ANNs) are composed of interconnected nodes that operate similarly to biological neurons. ANNs learn by adjusting the weights between nodes from examples to detect patterns in data. The history of ANNs began in the 1940s with early models of neural networks and research into biological neurons. Significant developments continued through the 1960s-1980s with multilayer perceptrons and backpropagation, leading to today's applications of ANNs to complex problems.
Artificial neural networks are a form of artificial intelligence inspired by biological neural networks. They are composed of interconnected processing units that can learn patterns from data through training. Neural networks are well-suited for tasks like pattern recognition, classification, and prediction. They learn by example without being explicitly programmed, similarly to how the human brain learns.
The document provides an introduction to neural networks, including:
- Biological neural networks transmit signals via neurons connected by synapses and axons.
- Artificial neural networks are composed of simple processing elements (neurons) that operate in parallel and are determined by network structure and connection strengths (weights).
- Multilayer neural networks consist of an input layer, hidden layers, and output layer connected by weights to solve complex problems. Learning involves updating weights so the network can efficiently perform tasks.
This document provides an overview of artificial neural networks. It discusses the biological neuron model that inspired artificial neural networks. The key components of an artificial neuron are inputs, weights, summation, and an activation function. Neural networks have an interconnected architecture with layers of nodes. Learning involves modifying the weights through algorithms like backpropagation to minimize error. Neural networks can perform supervised or unsupervised learning. Their advantages include handling complex nonlinear problems, learning from data, and adapting to new situations.
This document provides an overview of artificial neural networks (ANNs). It discusses ANN basics such as their structure being inspired by biological neural networks in the brain. The document covers different types of ANNs including feedforward and feedback networks. It also discusses ANN properties like learning strategies, applications, advantages like handling noisy data, and disadvantages like requiring training. The conclusion states that ANNs are flexible and suited for real-time systems due to their parallel architecture.
Neural networks are a new method of programming computers that are good at pattern recognition. They are inspired by the human brain and are composed of interconnected processing elements called neurons. Neural networks learn by example through adjusting synaptic connections between neurons. They can be trained to perform tasks like pattern recognition and classification. There are different types of neural networks including feedforward and feedback networks. Training involves adjusting weights to minimize error through algorithms like backpropagation. Neural networks are used in applications like data analysis, forecasting, and medical diagnosis.
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
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This document discusses neural networks and fuzzy control. It begins by defining neural networks and noting that they can be trained to recall responses learned during training when only input data is provided. Fuzzy logic can be incorporated to add flexibility by allowing vague inputs and general system boundaries. The document then discusses various neural network learning algorithms and applications of neuro-fuzzy systems. It notes some shortcomings of current algorithms and proposes other methods for more efficient control. The document also demonstrates how fuzzy parameters and principles can be added to a neural network to provide user flexibility and robustness.
Similar to Artificial neural-network-paper-presentation-100115092527-phpapp02 (20)
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Artificial Neural Network
INTRODUCTION
BACKGROUND:
co om
Many task which seem simple for us, such as reading a
handwritten note or recognizing a face, are difficult task for even the most
m
advanced computer. In an effort to increase the computer ability to perform
such task, programmers began designing software to act more like the
human brain, with its neurons and synaptic connections. Thus the field of
gi. .c
“Artificial neural network” was born. Rather than employ the traditional
method of one central processor (a Pentium) to carry out many instructions
one at a time, the Artificial neural network software analyzes data by passing
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it through several simulated processos which are interconnected with
synaptic like “weight”
Once we have collected several record of the data we wish to
analyze, the network will run through them and “learn ” the input of each
record may be related to the result. After training on a few doesn’t cases the
ntyy
network begin to organize and refines its on own architecture to feed the
eent
data to much the human brain; learn from example.
This “REVERSE ENGINEERING” technology were once regarded
as the best kept secret of large corporate, government an academic
researchers.
The field of neural network was pioneered by BERNARD
t t dd
WIDROW of Stanford University in 1950’s.
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Artificial Neural Network
Why would anyone want a `new' sort of computer?
What are (everyday) computer systems good at... .....and not so good at?
Good at Not so good at
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Interacting with noisy data or data
Fast arithmetic
from the environment
Doing precisely what the programmer
Massive parallelism
programs them to do
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Massive parallelism
Fault tolerance
Adapting to circumstances
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Where can neural network systems help?
• where we can't formulate an algorithmic solution.
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• where we can get lots of examples of the behaviour we require.
• where we need to pick out the structure from existing data.
What is a neural network?
Neural Networks are a different paradigm for computing:
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• Von Neumann machines are based on the processing/memory
abstraction of human information processing.
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• Neural networks are based on the parallel architecture of animal
brains.
Neural networks are a form of multiprocessor computer system, with
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• Simple processing elements
• A high degree of interconnection
• Simple scalar messages
• Adaptive interaction between elements
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Artificial neural network (ANNs) are programs designed to solve
any problem by trying to mimic structure and function of our nervous
system. Neural network are based on simulated neurons. Which are joined
together in a variety of ways to form networks. Neural network resembles
the human brain in the following two ways: -
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Artificial Neural Network
A neural network acquires knowledge through learning
A neural network’s knowledge is stored with in the interconnection strengths
known as synaptic weight.
Neural network are typically organized in layers. Layers are
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made up of a number of interconnected ‘nodes’, which contain an ‘activation
function’. Patterns are presented to the network via the ‘input layer’, which
communicates to one or more ‘hidden layers’ where the actual processing is
done via a system of weighted ‘connections’. The hidden layers then link to
an ‘output layer’ where the answer is output as shown in the graphic below.
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Hidden Layers
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Connections nty
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Input layer Output layer
Basic structure of neural network
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Each layer of neural makes independent computation on data that it receives
and passes the result to the next layers(s). The next layer may in turn make
independent computation and pass data further or it may end the
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computation and give the output of the overall computation .The first layer is
the input layer and the last one, the output layer. The layers that are placed
within these two are the middle or hidden layers.
A neural network is a system that emulates the cognitive
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abilities of the brain by establishing recognition of particular inputs and
producing the appropriate output. Neural networks are not “hard-wired” in
particular way; they are trained using presented inputs to establish their own
internal weights and relationships guided by feedback. Neural networks are
free to form their own internal working and adapt on their own.
Commonly neural network are adjusted, or trained so that a particular
input leads to a specific target output
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Artificial Neural Network
Target
Neural networksddss
Neural network
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Including
Input connections Compare
(called weights)
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Figure showing adjust of neural network
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There, the network is adjusted based on a comparison of the output and the
target, until the network output matches the target. Typically many such
input/target pairs are used to train network.
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Once a neural network is ‘trained’ to a satisfactory level it may be used as an
analytical tool on other data. To do this, the user no longer specifies any
training runs and instead allows the network to work in forward propagation
mode only. New inputs are presented to the input pattern where they filter
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into and are processed by the middle layers as though training were taking
place, however, at this point the output is retained and no back propagation
occurs.
The structure of Nervous system
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Nervous system of a human brain consists of neurons, which are
interconnected to each other in a rather complex way. Each neuron can be
thought of as a node and interconnection between them are edge, which has
a weight associates with it, which represents how mach the tow neuron
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which are connected by it can it interact.
Node (neuron)
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Edge
(interconnection)
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Artificial Neural Network
Functioning of A Nervous System
The natures of interconnections between 2 neurons can such that – one
neuron can either stimulate or inhibit the other. An interaction can take place
only if there is an edge between 2 neurons. If neuron A is connected to
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neuron B as below with a weight w, then if A is stimulated sufficiently, it
sends a signal to B. The signal depends on
w
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A B
The weight w, and the nature of the signal, whether it is stimulating or
inhibiting. This depends on whether w is positive or negative. If its
stimulation is more than its threshold. Also if it sends a signal, it will send it
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to all nodes to which it is connected. The threshold for different neurons
may be different.
If many neurons send signal to A, the combined stimulus may be more than
the threshold.
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Next if B is stimulated sufficiently, it may trigger a signal
to all neurons to which it is connected.
Depending on the complexity of the structure, the
overall functioning may be very complex but the functioning of individual
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neurons is as simple as this. Because of this we may dare to try to simulate
this using software or even special purpose hardware.
Major components Of Artificial Neuron
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This section describes the seven major components, which
make up an artificial neuron. These components are valid whether the
neuron is used for input, output, or is in one of the hidden layers.
Component 1. Weighing factors:
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A neuron usually receives many simultaneous inputs. Each input
has its own relative weight, which gives the input the impact that it needs on
the processing elements summation function. These weights perform the
same type of function, as do the varying synaptic strengths of biological
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neurons. In bath cases, some input are made more important than others so
that they have a greater effect on the processing element as they combine to
produce a neuron response.
Weights are adaptive coefficients within the network that determine
the intensity of the input signal as registered by the artificial neuron. They
are a measure of an input’s connection strength. These strengths can be
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Artificial Neural Network
modified in response to various training sets and according to a network’s
specific topology or through its learning rules.
Component 2. Summation Function:
The first step in a processing element’s operation is to compute the
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weighted sum of all of the inputs. Mathematically, the inputs and the
corresponding weights are vectors which can be represented as (i1,
i2,………….in) and (w1, w2,……………….wn). The total input signal is
the dot, or inner, product of these two vectors. This simplistic summation
function is found by multiplying each component of the i vector by the
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corresponding component of the w vector and then adding up all the
products. Input1 = i1*w1, input2=i2*w2, etc., are added as
input1+input2+………..+inputn. The result is a single number, not a multi-
element vector.
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Geometrically, the inner product of two vectors can be considered a
measure of their similarity. If the vector point in the same direction, the
inner product is maximum; if the vectors points in opposite direction (180
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degrees out of phase), their inner product is minimum.
The summation function can be more complex than just the simple input
and weight sum of products. The input and weighting coefficients can be
combined in many different ways before passing on to the transfer function.
In addition to a simple product summing, the summation function can select
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the minimum, maximum, majority, product, or several normalizing
algorithms. The specific algorithm for combining neural inputs is
determined by the chosen network architecture and paradigm.
Component 3: Transfer Function:
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The result of the summation function, almost always the weighted sum,
is transformed to a working out put through an algorithm process known as
the transfer function. In transfer function the summation total can be
compared with some threshold to determine the neural output. If the sum is
w.
greater than the threshold value, the processing element generates a signal. If
the sum of the input and weight product is less than the threshold, no signal
(no some inhibitory signal) is generated. Both types of response are
significant.
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A simple binary neuron with 3 inputs ( X [1], X [2] and X [3]) and 2
outputs (O [1] and O[2] ). Every neuron has a particular threshold. A neuron
fires only if the weighted sum of its input exceeds the threshold.
Sum=summation (W i * Xi )
If sum>=T then the output is
Output O[1] and O[2]= 1 if ( ∑ X[i] * W[i] >=T )
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Artificial Neural Network
=0 else
X [1]
W [1]
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X [2] W [2] (synaptic connection)
O [1]
X [3] W [3]
(neuron processing node)
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McCulloch-Pitts Neuron Model
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We can use different kind of threshold functions namely:
Sigmoid function : f (x)=1/(1+exp(-x))
Step function : f (x) =0 if x<T
=k if x>=T
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Ramp funtion : f (x)=ax+b
The transfer function could be something as simple as depending upon
whether the result of the summation function is positive or negative. The
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network could output zero and one, and minus one, or other numeric
combinations. The transfer function would then be a “hard limiter” or step
function.
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Hard limiter Ramping function
Y y 1
1 x
x
-1 -1
w.
x<0, y=-1 x<0, y=0
x>=0, y=1 0<=x<=1, y=x
x>1, y=1
ww
y sigmoid functions y
1 1
x x
-1,0
y = 1/(1+e-x) x>=0, y=1-1(1+x)
x<0, y=-1+1(1-x)
Figure : Sample Transfer Functions
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Artificial Neural Network
Component 4: Scaling and Limiting:
After the processing element’s transfer function, the result can pass
through additional processes which scale and limit. This scaling simply
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multiplies a scale factor times the transfer value, and then adds an offset.
Limiting is the mechanism, which ensures that the scaled result does not
exceed, and upper or lower bound. This limiting is in addition to the hard
limits that the original transfer function may have performed.
Component 5: Output function (competition)
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At Each processing element is allowed one output signal, which it
may output to hundreds of other neurons. This is the just like the biological
neuron, where there are many inputs and only one output action. Normally,
the output is directly equivalent to the transfer function’s result. Some
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network topologies, however, modify the transfer result to incorporate
competition among neighboring processing elements. Neurons are allowed
to complete with each other, inhibiting processing elements unless they have
great strength. Competition can occur one or both of two levels. First,
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competition determines which artificial neuron will be active, are provides
an output. Second, competitive inputs help determine which processing
elements will participate in the learning or adaptation process.
Component 6: Error function and back-propagated value:
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In most learning networks the difference between the current output
and the desired output is calculated. This raw error is then transformed by
the error function to match particular network architecture. The most basic
architectures use this error directly, but some square the error while retaining
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its sign, some cube the error, other paradigms modify the raw error to fit
their specific purposes. The artificial neuron’s error is then typically
propagated into the learning function of another processing element. This
error term is sometimes called the current error.
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The current error is typically propagated backwards to a previous layer.
Yet, this back-propagated value can be either the current error, the current
error scaled in some manner (obtained by the derivative of the transfer
function), or some desired output depending on the network type. Normally,
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this back-propagated value, after being scaled by the learning function, is
multiplied against each of the incoming connection weights to modify them
before the next learning cycle.
Component 7: Learning function :
The purpose of the learning function is to modify the variable
connection weights on the inputs of each processing elements according to
some neural base algorithm. This process of changing the weights of the
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Artificial Neural Network
inputs connections to achieve some desired results could also be called the
adaption function, as well as the learning mode.
Paradigms of learning—
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There are three broad paradigms of learning:
• Supervised
• Unsupervised (or self-organised)
• Reinforcement learning ( a special case of supervised learning ).
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Supervised learning:
The vast majority of the artificial neural network solutions have
been trained with supervision. In this mode, the actual output of a neural
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network is compared to the desired output. The network then adjusts
weights, which are usually randomly set to begin with, so that the next
iteration, or cycle, will produce a closer match between the desired and
the actual output. This learning method tries to minimize the current
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errors of all processing elements. This global error reduction is created
over time by continuously modifying the input weights until acceptable
network accuracy is reached.
Adaptive
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network O
X W
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Distant Learning signal
generater D
Ρ [d, 0]
Distance measure
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Block dig. Of supervised learning
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Unsupervised learning:
In supervised learning the system directly compares the network
output with a known correct or desired answer, whereas in unsupervised
learning the output is not known. Unsupervised training allows the
neurons to compete with each other until winner emerge. The resulting
values of the winner neurons determine the class to which a particular
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Artificial Neural Network
data set belongs. Unsupervised learning is the great promise of the future.
It shouts that computers could some day learn on their own in a true
robotic sense. Currently, this learning method is limited to networks
known as self organizing map. These kinds of networks are not in wide
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spread use.
Adaptive
network
x W o
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Block diagram of unsupervised learning
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Reinforcement learning:
Reinforcement learning is a form of supervised learning where
adopted neuron receives feedback from the environment that directly
influences learning.
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Learning law :
The following general learning rules is adopted in the neural network
studies:
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The weight vector wi =[Wi1, Wi2……………………………….Win] t
increases proportion to the product of input x and learning signal r. The
learning signal r is a function of wi x, and sometimes of the teacher’s signal
di.
Hence we have, r =r (Wi, X, di) and increment in weight vector produced
w.
by the learning step at time t is ∆Wi (t) =cr [Wi (t), X (t), di (t)] X (t)
Where c is learning constant.
Thus Wi (t+1) =Wi (t)+ cr [Wi (t), X (t), di (t)] X (t)
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Various learning rules are their assist the learning process.
They are :
1. Hebbian learning rule :
This rule represents purely feed forward, unsupervised learning
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Artificial Neural Network
th
X1 Wi1 i neuron
X2 Wi2 Oi
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Wi3 ∆Wi
X3 Learning di
Win x signal
r generater
Xn
i.c
c
Hebbian Learning Rule
According to this rule, we have
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r=f(Wti, X)
and increment of weight becomes
∆Wi=c f(Wti, X) X
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In this learning, the initial weight of neuron is.
2. Perceptron learning rule:
This learning is supervised and learning signal is equal to
r =di-Oi
Where Oi=sgn(Wti, X) and di is the desired response.
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Weight adjustment in this method is
∆Wi=c[di- sgn(Wti, X)] X
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in this method of learning, initial weight can have any value and neuron
much be binary bipolar are binary unipolar.
X1
X2 Wi1 TLU
w.
Wi2 neti
+ oi
ww
x3 Wi3 ∆Wi
Win di-oi + di
Xn X
C
Perception Learning Rule
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Artificial Neural Network
3. Delta learning rule:
This rule is valid for continuous activation functions and in the
supervised training mode. The learning signal is called as delta and is
given as:
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r=[di – f(Wti X)] f ′ (Wti X)
The adjustment for the single weight in this rule is given as:
∆Wi = c (di-Oi) f ′ (net i) X
In this method of learning, the initial weight can have any value and the
neuron must be continuous.
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X1
Wi1 continuous perception
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X2 wi2 f(neti) Oi
Win
Xn ∆Wi f΄(net i)
+
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X
r di - oi + di
c
Delta Learning Rule
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4. Windrow-Hoff learning rule:
This is applicable for the supervised training of neural networks and is
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independent of activation function.
Learning signal is given as :
r = di – Wti X
The weight vector increment under this learning rule is :
∆Wi=c (di - Wti X) X
w.
5. Correlation learning rule :
Substituting r = di in general learning rule, we obtain correlation learning
rule. The adjustment for the weight vector is given by:
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∆Wi = c di X
6. Winner-take all learning rule:
This rule is applicable for an ensemble of neurons, let’s say being
arranged in a layer of p units. This learning is base on the premise that
one of the neurons in the layer, say the mth, has the maximum response
due to input x, as shown in fig. This neuron is declared the winner. As a
result of this winning event, the weight vector Wm containing weights
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Artificial Neural Network
W11
X1 Wm1 O1
Wp1 “winning neuron”
W1j
Xj Wmj Om
om
Wpj
W1n Wmn
Xn Wpn Op
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Winner –take –All learning Rule
highlight in the fig. , by double headed arrow, is the only one adjusted in
the given unsupervised learning. It’s increment is computed as follows:
∆Wm = α (X - Wm)
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7.outstar learning rule:
This is an another learning rule that is best explained when the neurons
are arranged in layers. This rule is designed to produce a desired response
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d of the layer of p neurons as shown in fig… This rule is concerned with
the supervised learning and the weight adjustment is computed as:
∆Wj = β (X - Wj)
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X W11 o1
Wm1
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Wp1 ∆Wi +
+ d1
W1j β
Xj Wmj om
Wpj
w.
∆Wi +
+ dm
W1n Wmn β
ww
Xn op
Wpn ∆Wi +
+ dp
β
Outstar Learning Rule
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Artificial Neural Network
Training a neural network:
Since the output of the neural network may not be what is
expected, the network needs to be trained. Training involves altering the
interconnection weights between the neurons. A criterion is needed to
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specify when to change the weights and how to change them. Training is
an external process whiling learning is the process that takes place
internal to the network. The following guideline will be of help as a step
methodology for training a network.
• Choosing the number of neurons
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The number of hidden neurons affect how well the network is able
to separate the data. A large number of hidden neurons will ensure
correct learning and the network is able to correctly predict the data it has
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been trained on, but its performance on new data, its ability to generalize,
is compromised. With too few hidden neurons, the network may be
unable to learn the relationship amongst the data and the error will fail to
fall below an acceptable level. Thus, selection of the number of hidden
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neurons is a crucial decision. Often a trial and error approach is taken
starting with a modest number of hidden neurons and gradually
increasing the number if the network fails to reduce its error. A much
used approximation for the number of hidden neurons for a three layered
network is N=1/2(j + k)+v P, where J and K are the number of input
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neurons and P is the number of patterns in the training set.
• Choosing the initial weights
The learning algorithm uses steepest descent technique, which rolls
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straight downhill in weight space until the first valley is reached. This
valley may not correspond to a zero error for the resulting network. This
makes the choice of initial starting point in the multidimensional weight
space critical. However, there are no recommended rules for this
selection except trying several different weight values to see if the
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network results are improved.
• Choosing the learning rate
Learning rate effectively controls the size of the step that is taken
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in multidimensional weight space when each weight is modified. If the
selected learning rate is too large then the local minimum may be over
stopped constantly, resulting in oscillations and slow convergence to lower
error state. If the learning rate is too low, the number of iterations requires
may be too large, resulting in slow performance. Usually the default value of
most commercial neural network packages are in the range 0.1-0.3 providing
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Artificial Neural Network
a satisfactory balance between the two reducing the learning rate may help
improve convergence to a local minimum of the error surface.
• Choosing the activation function
The learning signal is a function of the error multiplied by the
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gradient of the activation function df/d (net). The larger the value
of the gradient, the more weight the learning will receive. For
training, the activation function must be monotonically increasing
from left to right, differentiable and smooth.
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Models Of artificial Neural Networks
There are different kinds of neural network models that can be used.
Some of the common ones are:
1. Perception model:
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This is a very simple model and consists of a single ‘trainable’
neuron. Trainable means that its threshold and input each input has a
desired output (determined by us). If the neuron doesn’t gives the desired
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output, then it has made a mistake. To rectify this, its threshold and/or
input weights must be changed. How this change is to be calculated is
determined by the learning algorithm.
The output of the perceptron is constrained to Boolean values – (true,
false), (1, 0), (1, -1) or whatever. This is not a limitation because if the
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output of the perceptron were to be the input for something else, then the
output edge could be made to have a weight. Then the output would be
dependant on this weight.
The perceptron looks like –
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X1
W1
X2 W2
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X3 W2
y
Xn Wn
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X1, X2, …………., Xn are inputs. These could be real numbers or Boolean
values depending on the problem.
y is the output and is Boolean.
w 1, w2, …………, wn are weights of the edges and are real valued.
T is the threshold and is a real valued.
The output y is 1 if the net input which is :
w1 x1 + w2 x2 + …….+ wn xn
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Artificial Neural Network
is greater than the threshold T. Otherwise the output is zero.
2. Feed – Forward Model:
Elementary feed forward architecture of m neurons and
receiving n inputs is shown in the figure. Its output and input vectors are
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respectfully.
O = [O] O2 ………Om]
X = [x] x2………..xn]
Weight Wij connects the ith neuron with the jth input. Hence activation
value net i for the ith neuron is
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Net i = j=1∑n Wij Xj for i=1, 2, 3,……, n
Hence, the output is given as
Oi = f(Wti X) for i = 1, 2, 3, ……….., m
Where Wi is the weight vector containing the weights leading towards
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the ith output node and defined as
Wi = [Wi1 Wi2 …………. Win]
If is the nonlinear matrix operarte, the mapping of input space X to
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output space O implemented by the network can be expressed
O = WX
Where W is the weight matrix, also called the connection matrix.
The generic feedforward network is characterized by the lack of
feedback. This type network can be connected in cascade to create a
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multiplayer network.
W11
X1 W21 O1
W12 1
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X2 W22
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W2n O2
Wm1 2
W1n Wm2
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Xn Wmn m On
X(t) ┌ [Wx] O(t)
Single –layer Feed forward network:interconnection scheme & block dig.
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Artificial Neural Network
W11 W12 ……..………W1n
W21 W22 ……..………W2n
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. . ……………. .
W= . . …………… .
. . …………… .
Wm1 …………….Wmn
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f(.) O…………..O
O f (.) ………..O
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┌ [.] =
. . ………… .
. . ………… .
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O O………….f(.)
3. Feed-Back model:
A feedback network can be obtained from the feedforawrd
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network by connecting the neuron’s outputs to their inputs, as shown in
the fig. The essence of closing the feedback loop is to hold control of
output Oi through outputs Oj; for j =1, 2, ……, m. or controlling the
output at instant t+∆ by the output at instant t. This delay ∆ is
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introduced by the delay element in the feedback loop. The mapping of
O(t) into O(t+ ∆) can now be written as
O(t+ ∆) = ┌[W o(t)]
w.
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Artificial Neural Network
O2 (t)
∆
O1 (t)
W11 ∆
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X1 W21 O1(t+ ∆)
W12
x2 W22 O2(t+ ∆)
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W2n
Lag free neuron
W1n Wm1
Wm2
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Xn Wmn O3(t+ ∆)
m
∆
Om(t)
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Instantaneous
X (0) network O (t+∆)
┌ [W o(t)]
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O (t)
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Delay
∆
Single –layer feedback network : interconnection scheme & block dig.
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Notations Used:
M1 is a 2-D matrix where M1[i] [j] represents the weight on the
connection from the ith input neuron to the jth neuron in the hidden layer.
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M2 is a 2 –D matrix where M2[i][j] denotes the weight on the
connection from the ith hidden layer neuron to the jth output layer neuron.
x, y and z are used to denote the outputs of neurons from the input
layer, hidden layer and output layer respectively.
If there are m input data, then (x1, x2, ……., xm). P denotes the desired
output pattern with components (p1, p2, ………, pr) for r outputs.
Let the number of hidden layer neurons be n.
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Artificial Neural Network
βo = learning parameter for the output layer.
βh = learning parameter for the hidden layer.
α = momentum term
θj = threshold value (bias) for the jth hidden layer neuron.
τj = threshold value for the jth output layer neuron.
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ej = error at the jth output layer neuron.
tj = error at the jth hidden layer neuron.
Threshold function = sigmoid function : F(x) = 1/(1 + exp(x)).
Mathematical expressions:
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Output of jth hidden layer neuron : yj = f((Σi xi M1[i][j]) + θj)
Output of jth output layer neuron : zj = f((Σi yi M2[i][j]) + τj).
ith component of vector of output differences:
desired value – computed value = Pj – zj
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th
i component of output error at the output layer:
ej = pj – zj.
th
i component of output error at the hidden layer:
ti = yi (1 – yi ) (Σj M2[i][j] ej )
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Adjustment of weights between the ith neuron in the hidden layer and jth
output neuron:
∆M2[i][j] (t) = β0 yi ej + α ∆M2 [i][j] (t - 1)
Adjustment of weights between the ith input neuron and jth neuron in the
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hidden layer :
∆M1[i][j] = βh xi tj + α ∆M1 [i][j] (t - 1)
Adjustment of the threshold value for the jth output neuron:
∆ τj = β0 ej
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Adjustment of the threshold value for the jth hidden layer neuron:
∆ θj = βh ej
Neural network applications:
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Aerospace
• High performance aircraft autopilot, flight path simulation, aircraft
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control systems, autopilot enhancements, aircraft component
simulation, aircraft component fault detection.
Automobile control
• Automobile automatic guidance system, warranty activity analysis.
Banking
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Artificial Neural Network
• Check and other document reading credit application
evaluation.
Credit card activity checking
• Neural networks are used to spot unusual credit card
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activity that might possibly be associated with loss of a
credit card
Defense
• Weapon steering, target tracking, object discrimination,
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facial recognition, new kinds of sensors, sonar, radar and
image signal processing including data compression,
feature extraction and noise suppression, signal/ image
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identification.
Electronics
• Code sequence prediction, integrated circuit chip laying,
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process control, chip failure analysis, machine vision
voice synthesis, nonlinear modeling.
Entertainment
• Animation, special effects, market forecasting.
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Financial
• Real estate appraisals, loan advisor, mortgage screening,
corporate bond rating, credit-line use analysis, and
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portfolio trading program, corporate financial analysis,
and currency price prediction.
Industrial
• Neural networks are being trained to predict the output
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gasses of furnaces and other industrial process. They then
replace complex and costly equipment used for this
purpose in the past.
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Insurance
• Neural networks are used in policy application evaluation,
product optimization.
Manufacturing
• Neural networks are used in manufacturing process
control, product design and analysis, process and machine
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Artificial Neural Network
diagnosis, real-time particle identification, visual quality
analysis, paper quality prediction, computer – chip quality
analysis, analysis of grinding operations, chemical product
design analysis, machine maintenance analysis, project
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bidding, planning and management, dynamic modeling of
chemical process system.
Medical
• Neural networks are used in breast cancer cell analysis,
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EEG and ECG analysis, prosthesis design, optimization of
transplant times, hospital expense reduction, hospital
quality improvement, and emergency room test
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advisement.
Oil and Gas
• Neural networks are used in exploration of oil and gas.
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Robotics
• Neural networks are used in trajectory control, forklift
robot, manipulator controllers, vision systems
Other application
• Artificial intelligence
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• Character recognition
• Image understanding
• Logistics
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• Optimization
• Quality Control
• Visualization
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Advantages and disadvantages Of ANN:
Advantage: --
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1. It involve human like thinking.
2. They handle noisy or missing data.
3. They create their own relationship amongst information – no
equations!
4. They can work with large number of variables or parameters.
5. They provide general solutions with good predictive accuracy.
6. System has got property of continuous learning.
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Artificial Neural Network
7. They deal with the non-linearity in the world in which we live.
Disadvantage –
1. In learning process time required may be in several months or years.
2. It is hard to implement.
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Conclusion –
In this present world of automation to automate systems neural
network and fuzzy logic systems are required. Fuzzy logic deals with
vagueness or uncertainty and neural network related to human like thinking.
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If we use only fuzzy systems or only NN complete automation is never
possible. The combination is suitable because fuzzy logic has tolerance for
impression of data, while neural networks have tolerance for noisy data. As
there are different advantages and disadvantages usage of NN thus we use
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combination of neural networks and Fuzzy logic in order to implement a real
world system without manual interference.
Artificial neural network, in the present scenario is novel in it’s
technological field and still we have to witness a large of it’s development in
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the upcoming era’s, whose speculations are not required, as it will speak for
themselves.
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Bibliography
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References books:--
• “Introduction to Artificial Neural Network”, by Jacek M. Zurada;
Jaico Publishing House, 1999.
• “An Introduction to Neural Network”, by James A. Anderson; PHI,
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1999.
• “Elements of Artificial Neural Network”, K. Mehrotra, C.K. Mohan
and Sanjay ranka, MIT Press, 1997.
• “Neural Network nad Fuzzy System”, by Bart Kosko; PHI, 1992.
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• “Neural Network – A comprehensive foundation”, Simon Haykin,
Macmillan Publishing Co., Newyork, 1993.
Reference sites –
• http://www.cs.stir.ac.uk/~lss/NNInro/invSlides.htm
• http://www.bitstar.com/nnet.htm
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• http://www.pmsi.fr/sxcxmpa.htm
• http://www.pmsi.fr/neurinia.htm
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