The document discusses soft computing and artificial neural networks. It provides an overview of soft computing techniques including artificial neural networks (ANNs), fuzzy logic, and evolutionary computing. It then focuses on ANNs, describing their biological inspiration from neurons in the brain. The basic components of ANNs are discussed including network architecture, learning algorithms, and activation functions. Specific ANN models are then summarized, such as the perceptron, ADALINE, and their learning rules. Applications of ANNs are also briefly mentioned.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
The document provides information about multi-layer perceptrons (MLPs) and backpropagation. It begins with definitions of perceptrons and MLP architecture. It then describes backpropagation, including the backpropagation training algorithm and cycle. Examples are provided, such as using an MLP to solve the exclusive OR (XOR) problem. Applications of backpropagation neural networks and options like momentum, batch vs sequential training, and adaptive learning rates are also discussed.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
1. A perceptron is a basic artificial neural network that can learn linearly separable patterns. It takes weighted inputs, applies an activation function, and outputs a single binary value.
2. Multilayer perceptrons can learn non-linear patterns by using multiple layers of perceptrons with weighted connections between them. They were developed to overcome limitations of single-layer perceptrons.
3. Perceptrons are trained using an error-correction learning rule called the delta rule or the least mean squares algorithm. Weights are adjusted to minimize the error between the actual and target outputs.
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
Deep learning uses neural networks, which are systems inspired by the human brain. Neural networks learn patterns from large amounts of data through forward and backpropagation. They are constructed of layers including an input layer, hidden layers, and an output layer. Deep learning can learn very complex patterns and has various applications including image classification, machine translation, and more. Recurrent neural networks are useful for sequential data like text and audio. Convolutional neural networks are widely used in computer vision tasks.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
The document provides information about multi-layer perceptrons (MLPs) and backpropagation. It begins with definitions of perceptrons and MLP architecture. It then describes backpropagation, including the backpropagation training algorithm and cycle. Examples are provided, such as using an MLP to solve the exclusive OR (XOR) problem. Applications of backpropagation neural networks and options like momentum, batch vs sequential training, and adaptive learning rates are also discussed.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
1. A perceptron is a basic artificial neural network that can learn linearly separable patterns. It takes weighted inputs, applies an activation function, and outputs a single binary value.
2. Multilayer perceptrons can learn non-linear patterns by using multiple layers of perceptrons with weighted connections between them. They were developed to overcome limitations of single-layer perceptrons.
3. Perceptrons are trained using an error-correction learning rule called the delta rule or the least mean squares algorithm. Weights are adjusted to minimize the error between the actual and target outputs.
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
Deep learning uses neural networks, which are systems inspired by the human brain. Neural networks learn patterns from large amounts of data through forward and backpropagation. They are constructed of layers including an input layer, hidden layers, and an output layer. Deep learning can learn very complex patterns and has various applications including image classification, machine translation, and more. Recurrent neural networks are useful for sequential data like text and audio. Convolutional neural networks are widely used in computer vision tasks.
https://github.com/dtemraz/machine-learning
Agenda:
- Quick start example
- Bayes theorem
- Naive Bayes classifier
- Text classification
- Case study SMS spam filter
- Alternative solutions
- Quick start example
- Bayes theorem
- Naive Bayes classifier
- Text classification
- Case study SMS spam filter
- Alternative solutions
This document provides an overview of convolutional neural networks (CNNs). It defines CNNs as multiple layer feedforward neural networks used to analyze visual images by processing grid-like data. CNNs recognize images through a series of layers, including convolutional layers that apply filters to detect patterns, ReLU layers that apply an activation function, pooling layers that detect edges and corners, and fully connected layers that identify the image. CNNs are commonly used for applications like image classification, self-driving cars, activity prediction, video detection, and conversion applications.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
Deep learning is a class of machine learning algorithms that uses multiple layers of nonlinear processing units for feature extraction and transformation. It can be used for supervised learning tasks like classification and regression or unsupervised learning tasks like clustering. Deep learning models include deep neural networks, deep belief networks, and convolutional neural networks. Deep learning has been applied successfully in domains like computer vision, speech recognition, and natural language processing by companies like Google, Facebook, Microsoft, and others.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
The document describes a vehicle detection system using a fully convolutional regression network (FCRN). The FCRN is trained on patches from aerial images to predict a density map indicating vehicle locations. The proposed system is evaluated on two public datasets and achieves higher precision and recall than comparative shallow and deep learning methods for vehicle detection in aerial images. The system could help with applications like urban planning and traffic management.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two common types of deep neural networks. RNNs include feedback connections so they can learn from sequence data like text, while CNNs are useful for visual data due to their translation invariance from pooling and convolutional layers. The document provides examples of applying RNNs and CNNs to tasks like sentiment analysis, image classification, and machine translation. It also discusses common CNN architecture components like convolutional layers, activation functions like ReLU, pooling layers, and fully connected layers.
The document discusses various activation functions used in deep learning neural networks including sigmoid, tanh, ReLU, LeakyReLU, ELU, softmax, swish, maxout, and softplus. For each activation function, the document provides details on how the function works and lists pros and cons. Overall, the document provides an overview of common activation functions and considerations for choosing an activation function for different types of deep learning problems.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
https://github.com/dtemraz/machine-learning
Agenda:
- Quick start example
- Bayes theorem
- Naive Bayes classifier
- Text classification
- Case study SMS spam filter
- Alternative solutions
- Quick start example
- Bayes theorem
- Naive Bayes classifier
- Text classification
- Case study SMS spam filter
- Alternative solutions
This document provides an overview of convolutional neural networks (CNNs). It defines CNNs as multiple layer feedforward neural networks used to analyze visual images by processing grid-like data. CNNs recognize images through a series of layers, including convolutional layers that apply filters to detect patterns, ReLU layers that apply an activation function, pooling layers that detect edges and corners, and fully connected layers that identify the image. CNNs are commonly used for applications like image classification, self-driving cars, activity prediction, video detection, and conversion applications.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
Deep learning is a class of machine learning algorithms that uses multiple layers of nonlinear processing units for feature extraction and transformation. It can be used for supervised learning tasks like classification and regression or unsupervised learning tasks like clustering. Deep learning models include deep neural networks, deep belief networks, and convolutional neural networks. Deep learning has been applied successfully in domains like computer vision, speech recognition, and natural language processing by companies like Google, Facebook, Microsoft, and others.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
The document describes a vehicle detection system using a fully convolutional regression network (FCRN). The FCRN is trained on patches from aerial images to predict a density map indicating vehicle locations. The proposed system is evaluated on two public datasets and achieves higher precision and recall than comparative shallow and deep learning methods for vehicle detection in aerial images. The system could help with applications like urban planning and traffic management.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two common types of deep neural networks. RNNs include feedback connections so they can learn from sequence data like text, while CNNs are useful for visual data due to their translation invariance from pooling and convolutional layers. The document provides examples of applying RNNs and CNNs to tasks like sentiment analysis, image classification, and machine translation. It also discusses common CNN architecture components like convolutional layers, activation functions like ReLU, pooling layers, and fully connected layers.
The document discusses various activation functions used in deep learning neural networks including sigmoid, tanh, ReLU, LeakyReLU, ELU, softmax, swish, maxout, and softplus. For each activation function, the document provides details on how the function works and lists pros and cons. Overall, the document provides an overview of common activation functions and considerations for choosing an activation function for different types of deep learning problems.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
Inroduction to Perceptron and how it is used in Machine Learning and Artificial Neural Network.
This presentation is prepared by Zaid Al-husseini, as a lectur for third stage of undergraduate students in Softwrae department - faculity of IT - University of Babylon, Iraq.
It is publicly availabe for the beginners to learn in theory and mathmatically how the Perceptron is working.
Notice: the slides are not detailed. And need a teacher to explain them deeply.
This document discusses neural networks and their learning capabilities. It describes how neural networks are composed of simple interconnected elements that can learn patterns from examples through training. Perceptrons are introduced as single-layer neural networks that can learn linearly separable functions through a simple learning rule. Multi-layer networks are shown to have greater learning capabilities than perceptrons using an algorithm called backpropagation that propagates errors backward through the network to update weights. Applications of neural networks include pattern recognition, control problems, and time series prediction tasks.
Neural networks are computing systems inspired by the human brain that are composed of interconnected nodes similar to neurons. They can recognize complex patterns in raw data through learning algorithms. An artificial neural network consists of layers of nodes - an input layer, one or more hidden layers, and an output layer. Weights are assigned to connections between nodes and are adjusted during training to produce the desired output.
1. The document discusses several approaches to cognitive science including connectionism, neural networks, supervised and unsupervised learning, Hebbian learning, the delta rule, backpropagation, and responses to Descartes from Gelernter, Penrose, and Pinker.
2. Connectionism models mental phenomena using interconnected networks of simple units like neural networks. Learning involves adjusting connection weights between neurons.
3. Supervised learning uses input-output pairs to adjust weights to minimize error, while unsupervised learning only uses inputs to find patterns in the data.
This document provides instructions for three exercises using artificial neural networks (ANNs) in Matlab: function fitting, pattern recognition, and clustering. It begins with background on ANNs including their structure, learning rules, training process, and common architectures. The exercises then guide using ANNs in Matlab for regression to predict house prices from data, classification of tumors as benign or malignant, and clustering of data. Instructions include loading data, creating and training networks, and evaluating results using both the GUI and command line. Improving results through retraining or adding neurons is also discussed.
The document provides an overview of artificial neural networks and supervised learning techniques. It discusses the biological inspiration for neural networks from neurons in the brain. Single-layer perceptrons and multilayer backpropagation networks are described for classification tasks. Methods to accelerate learning such as momentum and adaptive learning rates are also summarized. Finally, it briefly introduces recurrent neural networks like the Hopfield network for associative memory applications.
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.
This document discusses using an artificial neural network to forecast electricity demand. It describes preprocessing data, creating a feed-forward neural network model with input, hidden and output layers, and training the model using backpropagation and incremental training. The model is trained on 80% of the data and tested on the remaining 20%. Mean square error is used to evaluate accuracy on both the training and test sets, with a lower error on the test set indicating better generalization of the model to new data. The goal is to accurately forecast future electricity demand based on input variables like population, GDP, price indexes, and past consumption data.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural 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.
Multilayer Backpropagation Neural Networks for Implementation of Logic GatesIJCSES Journal
ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn
from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is
designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained
by using a backpropagation algorithm until the model satisfies the predefined error criteria (e)
which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The
NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For
OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively
The document describes using a neural network to predict food export and import data for India over 50 years using data from the UN's FAOSTAT organization. It presents case studies validating the neural network on synthetic sine wave and Duffing oscillator data. For food export prediction, the neural network was trained on 40 years of Indian export data for 10 food categories with 20 hidden nodes and sigmoid activation. Validation on the remaining 6 years showed the network's ability to predict actual export values.
Amnestic neural network for classificationlolokikipipi
1. The document presents an Amnesic Neural Network model for stock trend prediction that incorporates "forgetting" to address time variation in customer behavior data.
2. It applies the model to data on 900 stocks from 2001-2004 to classify price changes, training the network on 1000 records and testing it on 500.
3. The model achieved the highest classification accuracy of the test data with a forgetting coefficient of 0.1, outperforming a standard BP neural network model without forgetting.
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.
Similar to Artificial Neural Networks-Supervised Learning Models (20)
This document describes the ART1 neural network model, an unsupervised learning algorithm. ART1 performs clustering of binary input patterns. It has two layers of units (F1 and F2) with adaptive weights between them. The vigilance parameter ρ controls the level of similarity for patterns to be assigned to the same cluster. During training, an input is presented and the most active F2 unit is selected as the winner. Weights are updated only if the input and winner activation are sufficiently similar as determined by ρ. Otherwise, the network resets and searches for a better match. This continues until all patterns are clustered.
This document describes a genetic algorithm for finding the shortest path or tour in the traveling salesman problem (TSP). It introduces genetic algorithms and describes how they are applied to the TSP. The fitness measure calculates the total distance of a tour. Selection uses steady-state selection and crossover uses partially mapped crossover. Mutation uses swap mutation. The overall procedure initializes a population randomly, evaluates fitness, performs crossover and mutation, selects the next generation, and iterates until stopping criteria is met, outputting the best solution found. Experimental results on problem sizes of 10, 20, and 30 cities show the best and average tours found.
The document discusses deep learning and convolutional neural networks. It provides details on concepts like convolution, activation maps, pooling, and the general architecture of CNNs. CNNs are made up of repeating sequences of convolutional layers and pooling layers, followed by fully connected layers at the end. Convolutional layers apply filters to input images or feature maps from previous layers to extract features. Pooling layers reduce the spatial size to make representations more manageable.
This document describes the backpropagation algorithm for training multilayer artificial neural networks (ANNs). It discusses the key aspects of the backpropagation algorithm including: the initialization of weights and biases, feedforward propagation, backpropagation of error to calculate weight updates, and updating weights and biases. It provides pseudocode for the backpropagation training algorithm and discusses factors that affect learning like learning rate and momentum. It also gives an example of using backpropagation for load forecasting in power systems, showing the network architecture, training algorithm, and results.
The document discusses artificial neural networks (ANNs) and summarizes key information about soft computing techniques, ANNs, and some specific ANN models including perceptrons, ADALINE, and MADALINE. It defines soft computing as a collection of computational techniques including neural networks, fuzzy logic, and evolutionary computing. ANNs are modeled after the human brain and consist of interconnected neurons that can learn from examples. Perceptrons, ADALINE, and MADALINE are early ANN models that use different learning rules to update weights and biases.
The document discusses artificial neural networks (ANNs) and summarizes key information about ANNs and related topics. It defines soft computing as a field that aims to build intelligent machines using techniques like ANNs, fuzzy logic, and evolutionary computing. ANNs are modeled after biological neural networks and consist of interconnected nodes that can learn from data. Early ANN models like the perceptron, ADALINE, and MADALINE are described along with their learning rules and architectures. Applications of ANNs in various domains are also listed.
The importance of sustainable and efficient computational practices in artificial intelligence (AI) and deep learning has become increasingly critical. This webinar focuses on the intersection of sustainability and AI, highlighting the significance of energy-efficient deep learning, innovative randomization techniques in neural networks, the potential of reservoir computing, and the cutting-edge realm of neuromorphic computing. This webinar aims to connect theoretical knowledge with practical applications and provide insights into how these innovative approaches can lead to more robust, efficient, and environmentally conscious AI systems.
Webinar Speaker: Prof. Claudio Gallicchio, Assistant Professor, University of Pisa
Claudio Gallicchio is an Assistant Professor at the Department of Computer Science of the University of Pisa, Italy. His research involves merging concepts from Deep Learning, Dynamical Systems, and Randomized Neural Systems, and he has co-authored over 100 scientific publications on the subject. He is the founder of the IEEE CIS Task Force on Reservoir Computing, and the co-founder and chair of the IEEE Task Force on Randomization-based Neural Networks and Learning Systems. He is an associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
This presentation by Professor Giuseppe Colangelo, Jean Monnet Professor of European Innovation Policy, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdfBen Linders
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
Carrer goals.pptx and their importance in real lifeartemacademy2
Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.
This presentation by Tim Capel, Director of the UK Information Commissioner’s Office Legal Service, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by Katharine Kemp, Associate Professor at the Faculty of Law & Justice at UNSW Sydney, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
1. Soft Computing: Artificial
Neural Networks
Dr. Baljit Singh Khehra
Professor
CSE Department
Baba Banda Singh Bahadur Engineering College
Fatehgarh Sahib-140407, Punjab, India
2. Soft Computing
Soft Computing is a new field to construct new generation of AI , known as
Computational Intelligence.
Soft Computing is branch in which it is tried to build Intelligent Machines.
Hard Computing requires a precisely stated analytical model and often a lot
of computation time.
Many Analytical models are valid for ideal cases.
Real world problems exist in a non-ideal environment.
Soft Computing is a collection of methodologies that aim to exploit the
tolerance for imprecision and uncertainty to achieve tractability, robustness
and low solution cost.
The role model for Soft Computing is the human mind.
3. Soft Computing Techniques
Soft Computing is defined as collection of techniques spanning many fields
that fall under various categories in computational intelligence.
Soft Computing has main three branches:
Artificial Neural Networks (ANNs)
Fuzzy logic: To handle uncertainty (partial information about the problem, unreliable
information, information from more than one source about the problem that are conflicting)
Evolutionary Computing : contains optimization Algorithms
Genetic Algorithm (GA)
Ant Colony Optimization (ACO) algorithm
Biogeography based Optimization (BBO) approach
Bacterial foraging optimization algorithm
Gravitational search algorithm
Cuckoo optimization algorithm
Teaching-Learning-Based Optimization (TLBO)
Big Crunch Optimization (BBBCO) algorithm
4. Neural Networks (NNs)
A group of interconnected people that interact with each others to exchange
information.
CN is a group of two or more computer systems linked together to exchange
information.
A network of neurons
Neurons are the cells in the brain that convey information about the world around
us
A human brain has 86 billion neurons of different kinds.
But, we use only 10% of them.
6. Artificial Neural Networks (ANNs)
To simulate human brain behavior
Mimic information processing capability of Human Brain (Human
Nervous System).
Computational or Mathematical Models of Human Brain based on
some assumptions:
Information processing occurs at many simple elements called
Neurons.
Signals are passed b/w neurons by connection Links.
Each connection link has an Associated Weight.
The output of each neuron is obtained by passing its input through
Activation Function.
7. A Simple Artificial Neural Network
Activation function which is Binary Sigmoid function
)( inyfy
x
e
xf
1
1
)(
332211
3
1
wxwxwxwxy i
i
iin
inyin
e
yfy
1
1
)(
8. A Simple Artificial Neural Network with Multi-layers
Each ANN is composed of a collection of neurons grouped in layers.
Note the three layers: input, intermediate (called the hidden layer) and output.
Several hidden layers can be placed between the input and output layers.
)( inyfy
x
e
xf
1
1
)(
j
j
jin zvy
2
1
3
1i
iijinj xwz
)( injj zfz
9. Artificial Neural Networks (ANNs)
An ANN is characterized by
Its pattern of connections b/w neurons
(called its architecture)
Its method of determining weights on connections
(Training or Learning Algorithm)
Its Activation function.
Features of ANN
Adaptive Learning
Self-organization
Real-Time operation
Fault Tolerance via redundant information coding.
Information processing is local
Memory is distributed:
Long term: Weights
Short term: Signal sends
10. Advantages of ANNs
Lower interpolation error
Good extrapolation capabilities.
Generalization ability
Fast response time in operational phase
Free from numerical instability
Learning not programming
Parallelism in approach
Distributed memory
Intelligent behavior
Capability to operate based on a multivariate and noisy or error prone
training data set.
Capability for modeling non-linear characteristics.
11. Applications of ANNs
Designing fuzzy logic controllers
Parameter estimation for nonlinear systems
Optimization methods in real time traffic control
Power system identification and control
Power Load forecasting
Weather forecasting
Solving NP-Hard problems
VLSI design
Learning the topology and weights of neural networks
Performance enhancement of neural networks
Distributed data base design
Allocation and scheduling on multi-computers.
Signature verification study
Computer assisted drug design
Computer-aided disease diagnosis system
CPU Job scheduling
Pattern Recognition
Speech Recognition
Finger print Recognition
Face Recognition
Character/ Digit Recognition
Signal processing applications in virtual instrumentation systems
12. Basic Building Blocks of ANNs
Network Architecture
Learning Algorithms
Activation Functions
Network Architecture: The arrangement of neurons into layers and the pattern of
connection within and in-between layer are called the architecture of the network.
Commonly used Network Architecture are
13. Learning of ANNs
Learning or training algorithms are used to set weights and bias in Neural
Networks.
Types of Learning
– Supervised learning
– Unsupervised learning
Supervised learning
• Learning with a teacher
• Learning by examples
Training set
Examples: Perceptron, ADALINE, MADALINE, Backpropagation etc.
15. Unsupervised Learning
Self-organizing
Clustering
– Form proper clusters by discovering the similarities and
dissimilarities among objects
Examples: Kohonen Self-organizing MAP, ART1,ART2 etc.
16. Activation Functions
Activation Function: Activation Function is used to calculate the output
response of a neuron.
Various types of activation functions
Step function
Hard Limiter function
19. Rosenblatt’s Perceptron
In 1962, Frank Rosenblatt developed an ANN called Perceptron.
Perceptron is a computational model of the retina of the eye.
Weights b/w S and A are fixed
Weights b/w A and R are adjusted by Perceptron Learning Rule.
Learning of Perceptron is supervised.
Training algorithm is suitable for either Bipolar or Binary input with Bipolar
target, fixed threshold and adjustable bias.
20. Perceptron Training Rule
For each training pattern, net calculates the response of the output unit.
The net determines whether an error occurred for the pattern.
This is done by comparing the calculated output with target value.
If an error occurred for a particular training pattern (y ≠ t), then weights are
changed according to the following formula:
wi (new) = wi (old)+ wi
b (new ) = b (old)+ b
where wi = α t xi
b = α t
t is target output value for the current training example
y is Perceptron output
α is small constant (e.g., 0.5) called learning rate
The role of the learning rate is to moderate the degree to which weights
are changed at each step.
21. Activation Function for Perceptron
Binary Step Activation Function
Output of Perceptron
Perceptron only handle tasks which are linearly separable
in
in
in
in
yif
yif
yif
yfy
1
0
1
)(
22. Perceptron Training Algorithm
Step1. Initialize weights and bias.
Set weights and bias to small random values
Set Learning rate (0 < α ≤ 1)
Set Threshold Value (θ)
Step2. While stopping condition is False, do Steps 3-8
Step3. For each training pair (s : t), do Steps 4-7
Step 4. Set activation of input units, i = 1, …..,n
xi = si
Step 5. Compute response of output unit
y-in = b + w1x1 + … + wnxn
y = f (y-in)
in
in
in
in
yif
yif
yif
yfy
1
0
1
)(
23. Perceptron Training Algorithm
Step.6 Update Weight and bias
If an error occurred for a particular training pattern (y ≠ t),
then, weights are changed according to the following formula:
wi (new) = wi (old)+ wi
b (new ) = b (old)+ b
where wi = α t xi
b = α t
t is target output value for the current training example
y is Perceptron output
α is small constant (e.g., 0.5) called learning rate
Else
wi (new) = wi (old)
b (new ) = b (old)
Step 7. Test stopping condition
24. Perceptron Testing Algorithm
Step1. Set calculated weights from training algorithm
Set Learning rate (0 < α ≤ 1)
Set Threshold Value (θ)
Step2. For each input and target (s : t), do Steps 3-5
Step 3. Set activation of input units, i = 1, …..,n
xi = si
Step 4. Compute response of output unit
y-in = b + w1x1 + … + wnxn
y = f (y-in)
Step.5 Calculate Error
E=(t – y)
in
in
in
in
yif
yif
yif
yfy
1
0
1
)(
25. Development of Perceptron for AND Function
Input Output
1 1 1
1 -1 -1
-1 1 -1
-1 -1 -1
Input Output
1 1 1
1 -1 -1
-1 1 -1
-1 -1 -1
26. Perceptron Training Algorithm for AND function
x=[1 1 -1 -1;1 -1 1 -1];
t=[1 -1 -1 -1];
w=[0 0];
b=0;
alpha=input('Enter Learning Rate=');
theta=input('Enter Threshold Value=');
epoch=0;
maxepoch=100;
27. while epoch<mepoch
for i = 1:4
yin=b*x(1,i)*w(1)+x(2,i)*w(2);
if yin>theta
y=1;
end
if yin<=theta & yin>=-theta
y = 0;
end
if yin<-theta
y = -1;
end
if y – t(i) ~= 0
for j = 1:2
w(j) = w(j) + alpha*t(i)*x(j, i);
end
b=b + alpha*t(i);
end
end
epoch=epoch+1;
end
28. disp('Perceptron for AND function');
disp('Final Weight Matrix');
disp(w);
disp('Final Bias');
disp(b);
OUTPUT
Enter Learning Rate=1
Enter Threshold Value=0.5
Perceptron for AND function
Final Weight Matrix
0 2
Final Bias
0
29. Perceptron Testing Algorithm for AND function
x=[1 1 -1 -1;1 -1 1 -1];
w=[0 2];
b=0;
for i=1:4
yin=b*x(1,i)*w(1)+x(2,i)*w(2);
if yin>theta
y(i)=1;
end
if yin<=theta & yin>=-theta
y(i)=0;
end
if yin<-theta
y(i)=-1;
end
end
y
OUTPUT: 1 -1 1 -1
Input Target Actual
Output
1 1 1 1
1 -1 -1 -1
-1 1 -1 1
-1 -1 -1 -1
30. ADALINE
In 1960, Widrow and Hoff developed ADALINE.
It uses Bipolar (+1 or -1) activations for its input signals and target output.
Weights and bias are updated using Delta Rule.
wi (new) = wi (old)+ wi
b (new ) = b (old)+ b
where wi = α (t –y-in)xi
b = α (t –y-in)
t is target output value for the current training example
y-in is input of output unit
α is learning rate
31. ADALINE Training Algorithm
Step1. Initialize weights and bias.
Set weights and bias to small random values
Set Learning rate (0 < α ≤ 1)
Step2. While stopping condition is False, do Steps 3-7
Step3. For each training pair (s : t), do Steps 4-6
Step 4. Set activation of input units, i = 1, …..,n
xi = si
Step 5. Compute net input of output unit
y-in = b + w1x1 + … + wnxn
Step.6 Update Weight and bias
wi (new) = wi (old) + α(t-y-in) xi
b (new ) = b (old) + α(t-y-in)
Step 7. Test stopping condition
32. ADALINE Testing Algorithm
Step1. Set calculated weights from training algorithm
Set Learning rate (0 < α ≤ 1)
Step2. For each input and target (s : t), do Steps 3-5
Step 3. Set activation of input units, i = 1, …..,n
xi = si
Step 4. Compute response of output unit
y-in = b + w1x1 + … + wnxn
y = f (y-in)
Step.5 Calculate Error
E=(t – y)
01
01
)(
in
in
in
yif
yif
yfy
33. MADALINE
In 1960, Widrow & Hoff developed MADALINE.
Many ADALINES arranged in a multilayer net.
A MADALINE with two hidden ADALINES and one output ADALINE.
MADALINE uses Bipolar (+1 or -1) activations for its input signals and target
output.
Weights and bias on output ADALINE are fixed.
Weights and bias on hidden ADALINES are updated using Widrow & Hoff rule.
34. MADALINE
Activation Function
Weights and bias on output ADALINE are fixed: v1 = v2 = b3 = 0.5
Weights and bias on hidden ADALINES are updated using Widrow & Hoff rule:
If t = y,
then, no weights and bias are updated
Otherwise
If t = 1,
then, weights and bias are updated on zJ (unit whose net input is closed to 0)
wiJ (new) = wiJ (old) + α (t –z-inJ)xi
bJ (new ) = bJ (old) + α (t –z-inJ)
If t = -1,
then, weights and bias are updated on zK (unit whose net input is +tive)
wiK (new) = wiK (old) + α (t –z-inK)xi
bK (new ) = bK (old) + α (t –z-inK)
01
01
)(
in
in
in
yif
yif
yfy
35. MADALINE Training Algorithm
Step1. Initialize weights and bias.
Set weights and bias on output units to v1 = v2 = b3 = 0.5
Set weights and bias on hidden ADALINES to small random values
Set Learning rate (0 < α ≤ 1)
Step2. While stopping condition is False, do Steps 3-10
Step3. For each training pair (s : t), do Steps 4-9
Step 4. Set activation of input units, i = 1, …..,n
xi = si
Step 5. Compute net input of each hidden ADALINE unit
z-in1 = b1 + w11x1 +w21x2
z-in2 = b2 + w12x1 +w22x2
Step 6. Determine output of each hidden ADALINE unit
z1 = f (z-in1)
z2 = f (z-in2)
36. MADALINE Training Algorithm
Step 7. Compute net input of the output ADALINE unit
y-in = b3 + v1z1 +v2z2
Step 8. Determine output of the output ADALINE unit
y = f (y-in)
Step9. Update Weights and bias using Widrow & Hoff rule:
If t = y, then, no weights and bias are updated
Otherwise
If t = 1, then, weights and bias are updated on zJ
wiJ (new) = wiJ (old) + α (1 –z-inJ)xi
bJ (new ) = bJ (old) + α (t –z-inJ)
If t = -1, then, weights and bias are updated on zK
wiK (new) = wiK (old) + α (-1 –z-inK)xi
bK (new ) = bK (old) + α (-1 –z-inK)
Step10. Test stopping condition
37. MADALINE Testing Algorithm
Step1. Set calculated weights from training algorithm
Set Learning rate (0 < α ≤ 1)
Step2. For each input and target (s : t), do Steps 3-8
Step 3. Set activation of input units, i = 1, …..,n
xi = si
Step 4. Compute net input of each hidden ADALINE unit
z-in1 = b1 + w11x1 +w21x2
z-in2 = b2 + w12x1 +w22x2
Step 6. Determine output of each hidden ADALINE unit
z1 = f (z-in1)
z2 = f (z-in2)
Step 7. Compute response of output unit
y-in = b3 + v1z1 +v2z2
y = f (y-in)
Step.8 Calculate Error
E=(t – y)
01
01
)(
in
in
in
yif
yif
yfy
38. MADALINE Training Algorithm for XOR function
Step 1.
w11=0.05, w21=0.2,b1=0.3
w12=0.1,w22=0.2, b2=0.15
v1 = v2 = b3 = 0.5
α=0.5
Step2. Begin Training, do Steps 3-10
Step3. For 1st training pair (s : t) = (1 1:-1), do Steps 4-9
Step 4. Activation of input units, i = 1, 2
xi = si
x1 = 1, x2 = 1
Step 5. Compute net input of each hidden ADALINE unit
z-in1 = b1 + w11x1 +w21x2 z-in1 = 0.3+0.05b+0.2=0.55
z-in2 = b2 + w12x1 +w22x2 z-in2 = 0.15+0.1+0.2=0.45
Step 6. Determine output of each hidden ADALINE unit
z1 = f (z-in1) z1 = 1
z2 = f (z-in2) z2 = 1
Input Target
s1 s2 t
1 1 -1
1 -1 1
-1 1 1
-1 -1 -1
39. MADALINE Training Algorithm for XOR function
Step 7. Compute net input of the output ADALINE unit
y-in = b3 + v1z1 +v2z2 y-in = 0.5 + 0.5 +0.5=1.5
Step 8. Determine output of the output ADALINE unit
y = f (y-in) y = 1
Step9. Update Weights and bias because Error occurred (t-y=-1-1=-2)
If t = -1, then, weights and bias are updated on zK
(unit whose net input is +tive)
wiK (new) = wiK (old) + α (-1 –z-inK)xi
bK (new ) = bK (old) + α (-1 –z-inK)
b1 (new ) = b1 (old) + α (-1 –z-in1)=0.3+0.5(-1-0.55)= - 0.475
w1 1(new ) = w1 1(old) + α (-1 –z-in1) x1=0.05+0.5(-1-0.55)1= -0.725
Similarly
w21(new) = -.0575, b2(new) = -0.575
w12(new) = -0.625, w22(new) = -0.525
Step10. Test stopping condition
40. MADALINE Training Algorithm for XOR function
After 1st Training pair of 1st Iteration, New Weights and bias
w11= -0.725, w21= -0.575, b1= -0.475
w12= -0.625, w22= -0.525, b2= -0.575
These weights and bias are used for 2nd training pair (1 -1: 1) in 1st iteration to get
new weights and bias.
New weights and bias obtained from 2nd training pair are used for 3rd training pair
(-1 1: 1) in 1st iteration to get new weights and bias.
New weights and bias obtained from 3rd training pair are used for 4th training pair
(-1 -1: -1) in 1st iteration and get new weights and bias.
Thus 1st Iteration is completed
weights and bias obtained in 1st Iteration (obtained from 4th training pair ) are used
for 1st training pair in 2nd Iteration to get new weights and bias
Step10. Test stopping condition