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.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
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.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
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.
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.
The document discusses artificial neural networks (ANNs). It provides an overview of ANNs, including their biological inspiration from neurons in the brain, their composition of interconnected processing elements called neurons, and how they are configured for applications like pattern recognition. The document also covers different types of ANNs, their computational power, capacity for learning, convergence abilities, and use for generalization. Examples are given of ANN applications in various business domains like marketing, sales forecasting, finance, insurance, and telecommunications. Risks of ANNs discussed include needing a large and diverse training set, overfitting data, and high hardware resource requirements. A hybrid symbolic-neural network approach is also mentioned.
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.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
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.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
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.
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.
The document discusses artificial neural networks (ANNs). It provides an overview of ANNs, including their biological inspiration from neurons in the brain, their composition of interconnected processing elements called neurons, and how they are configured for applications like pattern recognition. The document also covers different types of ANNs, their computational power, capacity for learning, convergence abilities, and use for generalization. Examples are given of ANN applications in various business domains like marketing, sales forecasting, finance, insurance, and telecommunications. Risks of ANNs discussed include needing a large and diverse training set, overfitting data, and high hardware resource requirements. A hybrid symbolic-neural network approach is also mentioned.
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 computational models inspired by the human brain. They consist of interconnected nodes that process information using a principle called neural learning. The document discusses the history and evolution of neural networks. It also provides examples of applications like image recognition, medical diagnosis, and predictive analytics. Neural networks are well-suited for problems that are difficult to solve with traditional algorithms like pattern recognition and classification.
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.
1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina.
2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain.
3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
Introduction Of Artificial neural networkNagarajan
The document summarizes different types of artificial neural networks including their structure, learning paradigms, and learning rules. It discusses artificial neural networks (ANN), their advantages, and major learning paradigms - supervised, unsupervised, and reinforcement learning. It also explains different mathematical synaptic modification rules like backpropagation of error, correlative Hebbian, and temporally-asymmetric Hebbian learning rules. Specific learning rules discussed include the delta rule, the pattern associator, and the Hebb rule.
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.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
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.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
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.
This document contains 40 questions about soft computing concepts including neural networks, fuzzy systems, evolutionary computation, and hybrid intelligent systems. The questions cover topics such as the differences between hard and soft computing, components of expert systems, applications of artificial neural networks, types of learning in neural networks, perceptrons, adaptive linear neurons, backpropagation networks, and training algorithms for various neural network architectures.
VLSI for neural networks and their applications was presented. Biological neural networks refer to networks of biological neurons that perform physiological functions. Artificial neural networks are mathematical models inspired by biological neural networks. Neural networks can be digital, analog, or hybrid and have applications in areas like pattern and speech recognition, economy, sociology, and basic sciences like investigating the impact of treatments over time. In conclusion, artificial neural networks that simulate human biological neurons have potential for wide implementation and can be trained on input data and then apply that knowledge to new cases.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
This document discusses various applications of neural networks, including pattern recognition, autonomous vehicles, medicine, sports prediction, and virus detection. Some key applications mentioned are using neural networks for patient diagnosis, detecting coronary artery disease from medical images, predicting sports outcomes based on team statistics, and forecasting space weather events. The document also notes some limitations of neural networks, such as requiring large datasets and not providing explanations for decisions.
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.
This document provides an overview of artificial neural networks. It discusses how ANNs are inspired by biological neural systems and composed of interconnected processing elements called neurons. ANNs are configured through a learning process to perform tasks like pattern recognition or data classification. The document outlines the basic components of ANNs, including different types of network architectures like feedforward and feedback networks. It provides examples of applications for ANNs, such as speech and image recognition. In conclusion, it discusses using ANNs for applications in fields like medicine and business.
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.
An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
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.
Application Methods artificial neural network(Ann) Back propagation structure...irjes
This document describes a study that used an artificial neural network with backpropagation (ANN-BP) to predict Manning's roughness coefficient.
- The ANN-BP model was trained on 352 data points from laboratory experiments measuring flow parameters. It used a 7-10-1 network architecture with 10 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer.
- The model achieved a correlation coefficient of 0.980 when comparing predicted and simulated roughness coefficients. The mean squared error was 0.00000177 and the Nash-Sutcliffe efficiency value was 0.597, indicating good model performance.
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
Neural networks are computational models inspired by the human brain. They consist of interconnected nodes that process information using a principle called neural learning. The document discusses the history and evolution of neural networks. It also provides examples of applications like image recognition, medical diagnosis, and predictive analytics. Neural networks are well-suited for problems that are difficult to solve with traditional algorithms like pattern recognition and classification.
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.
1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina.
2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain.
3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
Introduction Of Artificial neural networkNagarajan
The document summarizes different types of artificial neural networks including their structure, learning paradigms, and learning rules. It discusses artificial neural networks (ANN), their advantages, and major learning paradigms - supervised, unsupervised, and reinforcement learning. It also explains different mathematical synaptic modification rules like backpropagation of error, correlative Hebbian, and temporally-asymmetric Hebbian learning rules. Specific learning rules discussed include the delta rule, the pattern associator, and the Hebb rule.
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.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
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.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
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.
This document contains 40 questions about soft computing concepts including neural networks, fuzzy systems, evolutionary computation, and hybrid intelligent systems. The questions cover topics such as the differences between hard and soft computing, components of expert systems, applications of artificial neural networks, types of learning in neural networks, perceptrons, adaptive linear neurons, backpropagation networks, and training algorithms for various neural network architectures.
VLSI for neural networks and their applications was presented. Biological neural networks refer to networks of biological neurons that perform physiological functions. Artificial neural networks are mathematical models inspired by biological neural networks. Neural networks can be digital, analog, or hybrid and have applications in areas like pattern and speech recognition, economy, sociology, and basic sciences like investigating the impact of treatments over time. In conclusion, artificial neural networks that simulate human biological neurons have potential for wide implementation and can be trained on input data and then apply that knowledge to new cases.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
This document discusses various applications of neural networks, including pattern recognition, autonomous vehicles, medicine, sports prediction, and virus detection. Some key applications mentioned are using neural networks for patient diagnosis, detecting coronary artery disease from medical images, predicting sports outcomes based on team statistics, and forecasting space weather events. The document also notes some limitations of neural networks, such as requiring large datasets and not providing explanations for decisions.
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.
This document provides an overview of artificial neural networks. It discusses how ANNs are inspired by biological neural systems and composed of interconnected processing elements called neurons. ANNs are configured through a learning process to perform tasks like pattern recognition or data classification. The document outlines the basic components of ANNs, including different types of network architectures like feedforward and feedback networks. It provides examples of applications for ANNs, such as speech and image recognition. In conclusion, it discusses using ANNs for applications in fields like medicine and business.
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.
An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
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.
Application Methods artificial neural network(Ann) Back propagation structure...irjes
This document describes a study that used an artificial neural network with backpropagation (ANN-BP) to predict Manning's roughness coefficient.
- The ANN-BP model was trained on 352 data points from laboratory experiments measuring flow parameters. It used a 7-10-1 network architecture with 10 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer.
- The model achieved a correlation coefficient of 0.980 when comparing predicted and simulated roughness coefficients. The mean squared error was 0.00000177 and the Nash-Sutcliffe efficiency value was 0.597, indicating good model performance.
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
The document describes a study that uses artificial neural networks (ANN), fuzzy inference systems (FIS), and adaptive neuro-fuzzy inference systems (ANFIS) to model and predict groundwater levels in the Thurinjapuram watershed in Tamil Nadu, India. Monthly rainfall and water level data from 1985 to 2008 were used as inputs, with one month ahead water level as the output. ANFIS performed best with lower error rates and higher correlation than ANN and FIS models according to statistical evaluations. Validation with unused 2009-2010 data showed ANFIS predictions were 80% accurate.
ANN Modeling of Monthly and Weekly Behaviour of the Runoff of Kali River Catc...IOSR Journals
Model is a system, by whose operation; the characteristics of other similar systems can be ascertained. Experimental observation made on a model bear a definite relationship with prototype. So, the model analysis or modeling is actually an experimental method of finding solution of complex flow problems like surface water modeling, sub-surface water modeling etc. Many flow situations are not amenable to theoretical analysis. Modeling is a valuable means of obtaining better understanding of particular situation. Inspired by the functioning of the brain and biological nervous system, Artificial Neural Networks (ANNs) has been applied to various hydrological problems in last two decades. In this study, two ANN models using feed forward – back propagation network are developed to correlate a relationship between rainfall and runoff on monthly and weekly basis for Kali river catchment up to Supa dam in Uttara Kannada District of Karnataka State, India. The developed two models are compared and evaluated using standard statistical parameters to know strength and weaknesses. This performance can be further refined by incorporating more input parameters of catchment properties like soil moisture index; land use and land cover details etc.
- To study the behavior and properties of bio-electric signals.
- Develop a system to identify and recognize patterns of signals on a portable computer.
This document summarizes a study that used an adaptive neuro-fuzzy inference system (ANFIS) to model rainfall-runoff in the Nagwan watershed in India. Daily rainfall and runoff data from 1993-1999 were used to train ANFIS models with different combinations of rainfall and runoff as inputs and current day runoff as the output. The best performing model used only current day rainfall as the input and had 91 Gaussian membership functions. This model had low root mean square error and high correlation for both the training and testing periods. The study demonstrated that ANFIS is effective for hydrological rainfall-runoff modeling in small watersheds where runoff is highly dependent on current day rainfall.
Neural network for real time traffic signal controlSnehal Takawale
This document describes a neural network system for real-time traffic signal control using a hybrid multi-agent approach. It discusses previous work using neural networks and stochastic approximation for traffic control. The proposed system uses multiple sensors to continuously learn traffic patterns and control signals. Simulation results over 3, 6, and 24 hours showed the hybrid neural network approach had lower mean delays than other methods, especially over longer periods as traffic fluctuated. While this system can automatically control traffic with minimal maintenance, limitations include potential unsuitability for areas with less variable traffic and high implementation costs.
The document provides an overview of neuromarketing, including its history, techniques, case studies, critiques, and future perspectives. Neuromarketing uses technologies like EEGs and fMRIs to measure brain activity and understand consumers' emotional engagement and memory formation related to products and ads. A key case study examined consumer preferences when drinking Coke vs Pepsi while being scanned; findings revealed cultural influences on brand perceptions. Both pros and cons are discussed, such as its ability to target the unconscious mind but also potential ethical issues with "brainwashing" techniques. The future of neuromarketing is debated, with some believing it will become standard for product development while others question if added regulation may be needed.
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.
The document discusses artificial neural networks and electronic noses. It describes how electronic noses use sensor arrays and neural networks to identify chemicals and odors. A prototype electronic nose is presented that uses 9 tin oxide sensors and neural networks to identify common household chemicals. Applications discussed include using electronic noses for medical diagnosis by analyzing odors from the body.
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptxJavier Daza
The document discusses reactive systems and neural networks. It defines reactive systems as software that rapidly responds to environmental events and changes. Neural networks are artificial intelligence models inspired by the human brain that consist of interconnected artificial neurons. They are used for tasks like machine learning, pattern recognition, and complex data processing. The document then goes into more detail about the structure of artificial neural networks and the components and functioning of artificial neurons.
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.
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.
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.
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.
This article provides an introduction to artificial neural networks (ANNs) and presents guidelines for designing effective ANN solutions. It discusses the key components of ANNs, including their biological inspiration, history, and different types of learning algorithms. The article emphasizes that successful ANN development requires extensive domain knowledge engineering and following best practices for selecting input variables, learning methods, architecture, and training samples. Specifically, it recommends knowledge-based input selection, choosing appropriate learning algorithms based on the data type, designing network topology based on the algorithm, and selecting optimal training set sizes, especially for time series problems. Overall, the article stresses that incorporating domain expertise at each design step is essential for building ANNs that generalize well to new problems.
This document discusses various techniques for data filtration and simulation using artificial neural networks. It provides an overview of zero-phase filtering, Kalman filtering, and empirical mode decomposition (EMD) as methods for adaptive data filtering. The zero-phase filter aims to minimize phase distortion while Kalman filtering is used as an error estimator. EMD decomposes signals into intrinsic mode functions (IMFs) in an adaptive manner. Alone, each method has limitations, but the document proposes that combining zero-phase filtering, Kalman filtering, and EMD can provide an effective solution by addressing their individual shortcomings. Examples are given to illustrate the application of these techniques on sample signals.
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.
Implementation of Feed Forward Neural Network for Classification by Education...ijsrd.com
in the last few years, the electronic devices production field has witness a great revolution by having the new birth of the extraordinary FPGA (Field Programmable Gate Array) family platforms. These platforms are the optimum and best choice for the modern digital systems now a day. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. In this paper a hardware design of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. Digital system architecture is designed to realize a feed forward multilayer neural network. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL).General Terms-Network.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
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 Network Implementation On FPGA ChipMaria Perkins
This document discusses implementing artificial neural networks on field programmable gate arrays (FPGAs). It begins with an abstract noting ANNs have been mostly implemented in software, but hardware versions can provide better performance. The document then reviews work done by various researchers on implementing ANNs using FPGAs. It discusses the structure of artificial neurons and feedforward neural networks. It also provides an overview of VHDL and describes different architectures for implementing artificial neurons in hardware using FPGAs. The goal is to help young researchers in implementing and realizing ANNs with hardware.
Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs can learn complex patterns and make predictions based on large amounts of data. The document discusses the basic structure and functioning of ANNs, including their ability to learn through adjustment of synaptic weights between neurons. It also describes several common types of ANNs, focusing on perceptrons and multi-layer perceptrons.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs can learn complex patterns and make predictions based on large amounts of data. The document discusses the basic structure and functioning of ANNs, including their ability to learn through adjustment of synaptic weights between neurons. It also describes several common types of ANNs, focusing on perceptrons and multi-layer perceptrons.
Artificial neural network are the mathematical inventions motivated by observation made in study of biological system, through loosely founded on the actual biology. An artificial neural network can be defined as mapping an input space to output space. This concept is analogous to that of mathematical function. The purpose of neural network is to map an input into desired output. Such a model has three simple sets of rules: multiplication, summation and activation. At the entrance of artificial neuron the inputs are weighted that means that every input value is multiplied with individual weight.
Abnormalities of hormones and inflammatory cytokines in women affected with p...Alexander Decker
Women with polycystic ovary syndrome (PCOS) have elevated levels of hormones like luteinizing hormone and testosterone, as well as higher levels of insulin and insulin resistance compared to healthy women. They also have increased levels of inflammatory markers like C-reactive protein, interleukin-6, and leptin. This study found these abnormalities in the hormones and inflammatory cytokines of women with PCOS ages 23-40, indicating that hormone imbalances associated with insulin resistance and elevated inflammatory markers may worsen infertility in women with PCOS.
A usability evaluation framework for b2 c e commerce websitesAlexander Decker
This document presents a framework for evaluating the usability of B2C e-commerce websites. It involves user testing methods like usability testing and interviews to identify usability problems in areas like navigation, design, purchasing processes, and customer service. The framework specifies goals for the evaluation, determines which website aspects to evaluate, and identifies target users. It then describes collecting data through user testing and analyzing the results to identify usability problems and suggest improvements.
A universal model for managing the marketing executives in nigerian banksAlexander Decker
This document discusses a study that aimed to synthesize motivation theories into a universal model for managing marketing executives in Nigerian banks. The study was guided by Maslow and McGregor's theories. A sample of 303 marketing executives was used. The results showed that managers will be most effective at motivating marketing executives if they consider individual needs and create challenging but attainable goals. The emerged model suggests managers should provide job satisfaction by tailoring assignments to abilities and monitoring performance with feedback. This addresses confusion faced by Nigerian bank managers in determining effective motivation strategies.
A unique common fixed point theorems in generalized dAlexander Decker
This document presents definitions and properties related to generalized D*-metric spaces and establishes some common fixed point theorems for contractive type mappings in these spaces. It begins by introducing D*-metric spaces and generalized D*-metric spaces, defines concepts like convergence and Cauchy sequences. It presents lemmas showing the uniqueness of limits in these spaces and the equivalence of different definitions of convergence. The goal of the paper is then stated as obtaining a unique common fixed point theorem for generalized D*-metric spaces.
A trends of salmonella and antibiotic resistanceAlexander Decker
This document provides a review of trends in Salmonella and antibiotic resistance. It begins with an introduction to Salmonella as a facultative anaerobe that causes nontyphoidal salmonellosis. The emergence of antimicrobial-resistant Salmonella is then discussed. The document proceeds to cover the historical perspective and classification of Salmonella, definitions of antimicrobials and antibiotic resistance, and mechanisms of antibiotic resistance in Salmonella including modification or destruction of antimicrobial agents, efflux pumps, modification of antibiotic targets, and decreased membrane permeability. Specific resistance mechanisms are discussed for several classes of antimicrobials.
A transformational generative approach towards understanding al-istifhamAlexander Decker
This document discusses a transformational-generative approach to understanding Al-Istifham, which refers to interrogative sentences in Arabic. It begins with an introduction to the origin and development of Arabic grammar. The paper then explains the theoretical framework of transformational-generative grammar that is used. Basic linguistic concepts and terms related to Arabic grammar are defined. The document analyzes how interrogative sentences in Arabic can be derived and transformed via tools from transformational-generative grammar, categorizing Al-Istifham into linguistic and literary questions.
A time series analysis of the determinants of savings in namibiaAlexander Decker
This document summarizes a study on the determinants of savings in Namibia from 1991 to 2012. It reviews previous literature on savings determinants in developing countries. The study uses time series analysis including unit root tests, cointegration, and error correction models to analyze the relationship between savings and variables like income, inflation, population growth, deposit rates, and financial deepening in Namibia. The results found inflation and income have a positive impact on savings, while population growth negatively impacts savings. Deposit rates and financial deepening were found to have no significant impact. The study reinforces previous work and emphasizes the importance of improving income levels to achieve higher savings rates in Namibia.
A therapy for physical and mental fitness of school childrenAlexander Decker
This document summarizes a study on the importance of exercise in maintaining physical and mental fitness for school children. It discusses how physical and mental fitness are developed through participation in regular physical exercises and cannot be achieved solely through classroom learning. The document outlines different types and components of fitness and argues that developing fitness should be a key objective of education systems. It recommends that schools ensure pupils engage in graded physical activities and exercises to support their overall development.
A theory of efficiency for managing the marketing executives in nigerian banksAlexander Decker
This document summarizes a study examining efficiency in managing marketing executives in Nigerian banks. The study was examined through the lenses of Kaizen theory (continuous improvement) and efficiency theory. A survey of 303 marketing executives from Nigerian banks found that management plays a key role in identifying and implementing efficiency improvements. The document recommends adopting a "3H grand strategy" to improve the heads, hearts, and hands of management and marketing executives by enhancing their knowledge, attitudes, and tools.
This document discusses evaluating the link budget for effective 900MHz GSM communication. It describes the basic parameters needed for a high-level link budget calculation, including transmitter power, antenna gains, path loss, and propagation models. Common propagation models for 900MHz that are described include Okumura model for urban areas and Hata model for urban, suburban, and open areas. Rain attenuation is also incorporated using the updated ITU model to improve communication during rainfall.
A synthetic review of contraceptive supplies in punjabAlexander Decker
This document discusses contraceptive use in Punjab, Pakistan. It begins by providing background on the benefits of family planning and contraceptive use for maternal and child health. It then analyzes contraceptive commodity data from Punjab, finding that use is still low despite efforts to improve access. The document concludes by emphasizing the need for strategies to bridge gaps and meet the unmet need for effective and affordable contraceptive methods and supplies in Punjab in order to improve health outcomes.
A synthesis of taylor’s and fayol’s management approaches for managing market...Alexander Decker
1) The document discusses synthesizing Taylor's scientific management approach and Fayol's process management approach to identify an effective way to manage marketing executives in Nigerian banks.
2) It reviews Taylor's emphasis on efficiency and breaking tasks into small parts, and Fayol's focus on developing general management principles.
3) The study administered a survey to 303 marketing executives in Nigerian banks to test if combining elements of Taylor and Fayol's approaches would help manage their performance through clear roles, accountability, and motivation. Statistical analysis supported combining the two approaches.
A survey paper on sequence pattern mining with incrementalAlexander Decker
This document summarizes four algorithms for sequential pattern mining: GSP, ISM, FreeSpan, and PrefixSpan. GSP is an Apriori-based algorithm that incorporates time constraints. ISM extends SPADE to incrementally update patterns after database changes. FreeSpan uses frequent items to recursively project databases and grow subsequences. PrefixSpan also uses projection but claims to not require candidate generation. It recursively projects databases based on short prefix patterns. The document concludes by stating the goal was to find an efficient scheme for extracting sequential patterns from transactional datasets.
A survey on live virtual machine migrations and its techniquesAlexander Decker
This document summarizes several techniques for live virtual machine migration in cloud computing. It discusses works that have proposed affinity-aware migration models to improve resource utilization, energy efficient migration approaches using storage migration and live VM migration, and a dynamic consolidation technique using migration control to avoid unnecessary migrations. The document also summarizes works that have designed methods to minimize migration downtime and network traffic, proposed a resource reservation framework for efficient migration of multiple VMs, and addressed real-time issues in live migration. Finally, it provides a table summarizing the techniques, tools used, and potential future work or gaps identified for each discussed work.
A survey on data mining and analysis in hadoop and mongo dbAlexander Decker
This document discusses data mining of big data using Hadoop and MongoDB. It provides an overview of Hadoop and MongoDB and their uses in big data analysis. Specifically, it proposes using Hadoop for distributed processing and MongoDB for data storage and input. The document reviews several related works that discuss big data analysis using these tools, as well as their capabilities for scalable data storage and mining. It aims to improve computational time and fault tolerance for big data analysis by mining data stored in Hadoop using MongoDB and MapReduce.
1. The document discusses several challenges for integrating media with cloud computing including media content convergence, scalability and expandability, finding appropriate applications, and reliability.
2. Media content convergence challenges include dealing with the heterogeneity of media types, services, networks, devices, and quality of service requirements as well as integrating technologies used by media providers and consumers.
3. Scalability and expandability challenges involve adapting to the increasing volume of media content and being able to support new media formats and outlets over time.
This document surveys trust architectures that leverage provenance in wireless sensor networks. It begins with background on provenance, which refers to the documented history or derivation of data. Provenance can be used to assess trust by providing metadata about how data was processed. The document then discusses challenges for using provenance to establish trust in wireless sensor networks, which have constraints on energy and computation. Finally, it provides background on trust, which is the subjective probability that a node will behave dependably. Trust architectures need to be lightweight to account for the constraints of wireless sensor networks.
This document discusses private equity investments in Kenya. It provides background on private equity and discusses trends in various regions. The objectives of the study discussed are to establish the extent of private equity adoption in Kenya, identify common forms of private equity utilized, and determine typical exit strategies. Private equity can involve venture capital, leveraged buyouts, or mezzanine financing. Exits allow recycling of capital into new opportunities. The document provides context on private equity globally and in developing markets like Africa to frame the goals of the study.
This document discusses a study that analyzes the financial health of the Indian logistics industry from 2005-2012 using Altman's Z-score model. The study finds that the average Z-score for selected logistics firms was in the healthy to very healthy range during the study period. The average Z-score increased from 2006 to 2010 when the Indian economy was hit by the global recession, indicating the overall performance of the Indian logistics industry was good. The document reviews previous literature on measuring financial performance and distress using ratios and Z-scores, and outlines the objectives and methodology used in the current study.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
1. Network and Complex Systems www.iiste.org
ISSN 2224-610X (Paper) ISSN 2225-0603 (Online)
Vol.3, No.1, 2013-Selected from Inter national Conference on Recent Trends in Applied Sciences with Engineering Applications
24
Artificial Neural Network
Neha Gupta
Institute of Engineerinf and Technology,DAVV,Indore
Email:-nehaguptaneema@gmail.com
ABSTRACT
The long course of evolution has given the human brain many desirable characteristics not present in Von
Neumann or modern parallel computers. These include massive parallelism, distributed representation and
computation, learning ability, generalization ability,adaptivity, inherent contextual information processing, fault
tolerance, and low energy consumption. It is hoped that devices based on biological neural networks will possess
some of these desirable characteristics.On this basic we come out with the concept of artificial neural network.
An artificial neural network, often just called a neural network, is a mathematical model inspired by biological
neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes
information using a connectionist approach to computation. Neural networks have emerged in the past few years
as an area of unusual opportunity for research, development and application to a variety of real world problems.
Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. The article
discusses the motivations behind the development of ANNs and describes the basic biological neuron. This
paper presents a brief tutorial on artificial neural networks, some of the most commonly used ANN models and
briefly describes several applications of it.
1. INTRODUCTION
Inspired by biological neural networks, ANNs are massively parallel computing systems consisting of an
exremely large number of simple processors with many interconnections. ANN models attempt to use some
“organizational” principles believed to be used in the human. One type of network sees the nodes as ‘artificial
neurons’. These are called artificial neural networks (ANNs). An artificial neuron is a computational model
inspired in the natural neurons. Since the function of ANNs is to process information, they are used mainly in
fields related with it. There are a wide variety of ANNs that are used to model real neural networks, and study
behaviour and control in animals and machines, but also there are ANNs which are used for engineering
purposes, such as pattern recognition, forecasting,and data compression. These basically consist of inputs (like
synapses), which are multiplied by weights. Weights assigned with each arrow represent information flow. These
weights are then computed by a mathematical function which determines the activation of the neuron. Another
function (which may be the identity) computes the output of the artificial neuron (sometimes in dependence of a
certain threshold). The neurons of this network just sum their inputs. Since the input neurons have only one input,
their output will be the input they received multiplied by a weight.
Biological Neuron
2. Network and Complex Systems
ISSN 2224-610X (Paper) ISSN 2225-0603 (Online
Vol.3, No.1, 2013-Selected from Inter national Conference on
If the weight is high,then input will be strong. By adjusting the weights of an artificial neuron we can obtain the
output we want for specific inputs. But when we have an ANN of hundreds or thousands of neurons, it would be
quite complicated to find by hand all the necessary weights. But we can find algorithms which can adjust the
weights of the ANN in order to obtain the desired output from the network. This process of adjusting the weights
is called learning or training. . The training begins with random
error will be minimal.
2.CHALLENGING PROBLEMS
a)Pattern recognization:
Pattern recognition is the assignment of a label to a given input value. Well known application include Other
typical applications of pattern recognition techniques are automatic
spam email messages), the automatic recognition of handwritten postal
recognition of images of human faces, or handwriting image extraction from medical forms.
b)Clustering/Categorization:
Categorization is the process in which ideas and objects are
and understood. Categorization implies that objects are grouped into categories,
purpose.applications of clustering include data mining,data compression and exploratory data analysis.
c)Prediction/Forecasting:
It has a significant impacton decition making in bussiness,science and engineering. Stock market
weather forecasting are typical application of prediction/forecasting techniques.
3. NETWORK ARCHITECTURE
Based on the connection pattern network architecture can be grouped into two categories :
• Feed-forward networks, in which graphs have
• Recurrent (or feedback) networks, in which loops occur because of feedback connections.
Generally,Feed-Forward Networks
sequence of values from a given input. Feedforwar
an input is independent of the previous network state.
Multilayer Perceptron Architecture:
network. “Fully connected” means that the output from each input and hidden neuron is distributed to all of the
neurons in the following layer. All neural networks have an input layer and an output layer, but the number of
hidden layers may vary. Here is a diagram of a perceptron ne
When there is more than one hidden layer, the output from one hidden layer is fed into the next hidden layer and
separate weights are applied to the sum going into each layer.
Radial Basis Function network: A
basis functions as activation functions
functions of the inputs and neuron parameters. Radial basis function networks are used for
approximation, time series prediction
(Online)
Inter national Conference on Recent Trends in Applied Sciences with Engineering Applications
25
Artificial Neuron
If the weight is high,then input will be strong. By adjusting the weights of an artificial neuron we can obtain the
output we want for specific inputs. But when we have an ANN of hundreds or thousands of neurons, it would be
nd all the necessary weights. But we can find algorithms which can adjust the
weights of the ANN in order to obtain the desired output from the network. This process of adjusting the weights
. . The training begins with random weights, and the goal is to adjust them so that the
is the assignment of a label to a given input value. Well known application include Other
of pattern recognition techniques are automatic recognition, classification
automatic recognition of handwritten postal codes on postal envelopes,
of human faces, or handwriting image extraction from medical forms.
in which ideas and objects are recognized
Categorization implies that objects are grouped into categories, usually for some specific
purpose.applications of clustering include data mining,data compression and exploratory data analysis.
It has a significant impacton decition making in bussiness,science and engineering. Stock market
weather forecasting are typical application of prediction/forecasting techniques.
3. NETWORK ARCHITECTURE
Based on the connection pattern network architecture can be grouped into two categories :
networks, in which graphs have no loops.
networks, in which loops occur because of feedback connections.
Forward Networks are static,that is, they produce only one set of output values
sequence of values from a given input. Feedforward networks are memory-less in the sense that their response to
an input is independent of the previous network state.
Multilayer Perceptron Architecture: This is a full-connected, three layer, feed-forward, perceptron neural
means that the output from each input and hidden neuron is distributed to all of the
neurons in the following layer. All neural networks have an input layer and an output layer, but the number of
hidden layers may vary. Here is a diagram of a perceptron network with two hidden layers and four total layers:
When there is more than one hidden layer, the output from one hidden layer is fed into the next hidden layer and
separate weights are applied to the sum going into each layer.
A radial basis function network is an artificial neural network
activation functions. The output of the network is a linear combination
functions of the inputs and neuron parameters. Radial basis function networks are used for
time series prediction, and system control.
www.iiste.org
Recent Trends in Applied Sciences with Engineering Applications
If the weight is high,then input will be strong. By adjusting the weights of an artificial neuron we can obtain the
output we want for specific inputs. But when we have an ANN of hundreds or thousands of neurons, it would be
nd all the necessary weights. But we can find algorithms which can adjust the
weights of the ANN in order to obtain the desired output from the network. This process of adjusting the weights
weights, and the goal is to adjust them so that the
is the assignment of a label to a given input value. Well known application include Other
recognition, classification (e.g. spam/non-
on postal envelopes, automatic
of human faces, or handwriting image extraction from medical forms.
recognized, differentiated,
usually for some specific
purpose.applications of clustering include data mining,data compression and exploratory data analysis.
It has a significant impacton decition making in bussiness,science and engineering. Stock market prediction and
Based on the connection pattern network architecture can be grouped into two categories :
networks, in which loops occur because of feedback connections.
that is, they produce only one set of output values rather than a
less in the sense that their response to
forward, perceptron neural
means that the output from each input and hidden neuron is distributed to all of the
neurons in the following layer. All neural networks have an input layer and an output layer, but the number of
twork with two hidden layers and four total layers:
When there is more than one hidden layer, the output from one hidden layer is fed into the next hidden layer and
artificial neural network that uses radial
linear combination of radial basis
functions of the inputs and neuron parameters. Radial basis function networks are used for function
3. Network and Complex Systems www.iiste.org
ISSN 2224-610X (Paper) ISSN 2225-0603 (Online)
Vol.3, No.1, 2013-Selected from Inter national Conference on Recent Trends in Applied Sciences with Engineering Applications
26
Issues: There are many issues in designing feed-forward networks:
How many layers are needed for a given task,
How many units are needed per layer,
How will the network perform on data not included in the training set (generalization ability), and
How large the training set should be for “good” generalization.
Although multilayer feed-forward networks using back propagation have been widely employed for
classification and function approximation.
Recurrent, or feedback, networks, on the other hand, are dynamic systems. When a new input pattern is
presented, the neuron outputs are computed. Because of the feedback paths, the inputs to each neuron are then
modified, which leads the network to enter a new state. Different network architectures require appropriate
learning algorithms.
4. OVERVIEW OF LEARNING PROCESSES:
A learning process in the ANN context can be viewed as the problem of updating network architecture and
connection weights so that a network can efficiently perform a specific task. The network usually must learn the
connection weights from available training patterns. Performance is improved over time by iteratively updating
the weights in the network. ANNs' ability to automatically learn from examples makes them attractive and
exciting. Instead of following a set of rules specified by human experts, ANNs appear to learn underlying rules
(like input-output relationships) from the given collection of representative examples. This is one of the major
advantages of neural networks over traditional expert systems.
Learning paradigms:
There are three major learning paradigms, each corresponding to a particular abstract learning task.These
are supervised learning, unsupervised learning and reinforcement learning.
Supervised learning the network is provided with a correct answer (output) for every input pattern.
Weights are determined to allow the network to produce answers as close as possible to the known
correct answers.
Unsupervised learning does not require a correct answer associated with each input pattern in the
training data set. It explores the underlying structure in the data, or correlations between patterns in the
data, and organizes patterns into categories from these correlations. Hybrid learning combines
supervised and unsupervised learning. Part of the weights are usually determined through supervised
learning, while the others are obtained through unsupervised learning.
Reinforcement learning is a variant of supervised learning in which the network is provided with only a
critique on the correctness of network outputs, not the correct answers themselves.
Error-Correction Rules:
In the supervised learning paradigm, the network is given a desired output for each input pattern. During the
learning process, the actual output generated by the network may not be equal to the desired output. The basic
principle of error-correction learning rules is to use the error signal (target output-obtained output) to modify the
connection weights to gradually reduce this error.
Perceptron learning rule:
The perceptron is an algorithm for supervised classification of an input into one of several possible non-binary
outputs. The learning algorithm for perceptrons is an online algorithm, in that it processes elements in the
training set one at a time. In the context of artificial neural networks, a perceptron is similar to a linear
neuron except that it does classification rather than regression. That is, where a perceptron adjusts its weights to
separate the training examples into their respective classes, a linear neuron adjusts its weights to reduce a real-
valued prediction error. The perceptron algorithm is also termed the single-layer perceptron, to distinguish it
from a multilayer perceptron, which is a misnomer for a more complicated neural network. As a linear classifier,
the single-layer perceptron is the simplest feedforward neural network.
Boltzmann learning
Boltzmann learning is derived from information-theoretic and thermodynamic principles.The objective of
Boltzmann learning is to adjust the connection weights so that the states of visible units satisfy a particular
desired probability distribution. Boltzmann learning can be viewed as a special case of error-correction learning
in which error is measured not as the direct difference between desired and actual outputs, but as the difference
between the correlations among the outputs of two neurons under clamped and free running operating conditions.
Hebbian Rule
The theory is often summarized as "Cells that fire together, wire together." Hebb's principle can be described as
a method of determining how to alter the weights between model neurons. The weight between two neurons
increases if the two neurons activate simultaneously and reduces if they activate separately. Nodes that tend to be
4. Network and Complex Systems www.iiste.org
ISSN 2224-610X (Paper) ISSN 2225-0603 (Online)
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either both positive or both negative at the same time have strong positive weights, while those that tend to be
opposite have strong negative weights.
Wi(new)=Wi(old)+XiY
For example, we have heard the word Nokia for many years, and are still hearing it. We are pretty used to
hearing Nokia Mobile Phones, i.e. the word 'Nokia' has been associated with the word 'Mobile Phone' in our
Mind. Every time we see a Nokia Mobile, the association between the two words 'Nokia' and 'Phone' gets
strengthened in our mind. The association between 'Nokia' and 'Mobile Phone' is so strong that if someone tried
to say Nokia is manufacturing Cars and Trucks, it would seem odd.
Competitive Wing Rules
Unlike Hebbian learning, competitive-learning output units compete among themselves for activation. As a
result, only one output unit is active at any given time. This phenomenon is known as winner-take-all.
Competitive learning has beenfound to exist in biological neural network.Competitive learning often clusters or
categorizes the input data. Similar patterns are grouped by the network and represented by a single unit. This
grouping is done automatically based on data correlations. The simplest competitive learning network consists of
a single layer of output units.
[Note: Only the weights of the winner unit get updated.]
The effect of this learning rule is to move the stored pattern in the winner unit (weights) a little bit closer to the
input pattern. From the competitive learning rule we can see that the network will not stop learning (updating
weights) unless the learning rate 0. A particular input pattern can fire different output units at different iterations
during learning. This brings up the stability issue of a learning system. The system is said to be stable if no
pattern in the training data changes its category after a finite number of learning iterations. One way to achieve
stability is to force the learning rate to decrease gradually as the learning process proceeds .
However, this artificial freezing of learning causes another problem termed plasticity, which is the
ability to adapt to new data. This is known as Grossberg’s stability plasticity dilemma in competitive learning.
Eg: Vector quantization for data compression: It has been widely used in speech and image processing for
efficient storage, transmission, and modeling. Its goal is to represent a set or distribution of input vectors with a
relatively small number of prototype vectors (weight vectors), or a codebook. Once a codebook has been
constructed and agreed upon by both the transmitter and the receiver, you need only transmit or store the index
of the corresponding prototype to the input vector.
The Back propagation Algorithm
The back propagation algorithm (Rumelhart and McClelland, 1986) is used in layered feed-forward ANNs. This
means that the artificial neurons are organized in layers, and send their signals “forward”, and then the errors are
propagated backwards. The network receives inputs by neurons in the input layer, and the output of the network
is given by the neurons on an output layer. There may be one or more intermediate hidden layers. The
backpropagation algorithm uses supervised learning. After finding error, we can adjust the weights using the
method of gradient descendent:
(formule of back prop algo)
For practical reasons, ANNs implementing the backpropagation algorithm do not have too many layers, since the
time for training the networks grows exponentially. Also, there are refinements to the backpropagation algorithm
which allow a faster learning.
5. APPLICATIONS
Although successful applications can be found in certain well-constrained environments, none is flexible enough
to perform well outside its domain. ANNs provide exciting alternatives, and many applications could benefit
from using them. Modern digital computers outperform humans in the domain of numeric computation and
related symbol manipulation.
Air traffic control could be automated with the location,altitude,direction and speed of each radar blip taken
as input to the network. The would be the air traffic controller’s instruction in response to each blip.
Animal behavior and population cycles may be suitable for analysis by neural networks. Appraisal and
valuation of property, building,automobiles…etc should be ab easy task for a neural network.
Echo pattern from sonar,radar and magnetic instruments could be used to predict their targets.
Weather prediction may be possible.
Voice recognization could be obtained by analyzing audio oscilloscope pattern.
Traffic flows could be predicted so that signal timing could be optimized…..many more.
CONCLUTION
Using artificial neural networks modern digital computers outperform humans in the domain of numeric
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ISSN 2224-610X (Paper) ISSN 2225-0603 (Online)
Vol.3, No.1, 2013-Selected from Inter national Conference on Recent Trends in Applied Sciences with Engineering Applications
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computation and related symbol manipulation.
REFERENCE
“Principles Of Soft Computing” by S. N. Sivanandam,S. N. Deepa,(2008).
“Soft Computing And Intelligent System Design-Theory Tools And Application” by Fakhreddine Karray
and Clarence De Silva (2004).
“Neural Network- A Comprehensive Foundation” by Simon S. Haykin. (1999).
“An Introduction To Neural Network”, by James A. Anderson,(1997).
“Fundamentals Of Neural Network” by Laurene V. Fausett,(1993).
Ash, T. (1989), ‘Dynamic node creation in backpropagation neural networks’, Connection Science 1(4),
365–375.
Jain,Anil K. Michigan State Univ., East Lansing,MI,USA Mao,Jianchang; Mohiuddin,K.M. (1996)
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