top of page

Upinox Trades Nigeri Group

Public·92 members

Introduction To Neural Networks Using Matlab 6 0 S N Sivanandam Sumathi Deepa


CLICK HERE >>> https://blltly.com/2tyd3t



Introduction To Neural Networks Using Matlab 6 0 S N Sivanandam Sumathi Deepa


Introduction to Neural Networks Using MATLAB 6.0 by S.N. Sivanandam, S. Sumathi and S.N. Deepa: A Book Review


Neural networks are computational models that mimic the structure and function of biological neurons and their connections. They can learn from data and perform tasks such as classification, regression, clustering, pattern recognition, image processing, natural language processing, and more.


One of the popular tools for developing and implementing neural networks is MATLAB, a high-level programming language and environment that offers a rich set of features and functions for numerical computation, visualization, and data analysis. MATLAB also provides a specialized toolbox for neural networks, called Neural Network Toolbox, that contains functions and GUIs for creating, training, testing, and visualizing various types of neural networks.


Introduction to Neural Networks Using MATLAB 6.0 is a book written by S.N. Sivanandam, S. Sumathi and S.N. Deepa, three professors from PSG College of Technology in India. The book is intended for undergraduate students in computer science who want to learn the basics of neural networks and their applications using MATLAB 6.0 and Neural Network Toolbox.


The book covers the following topics:


Fundamental models of artificial neural networks, such as perceptrons, multilayer perceptrons, radial basis function networks, recurrent networks, Hopfield networks, self-organizing maps, learning vector quantization, and adaptive resonance theory.


Learning algorithms for training neural networks, such as gradient descent, backpropagation, conjugate gradient, Levenberg-Marquardt, resilient backpropagation, quickprop, genetic algorithms, simulated annealing, particle swarm optimization, and ant colony optimization.


Applications of neural networks to various domains, such as bioinformatics, robotics, communication systems, image processing, speech recognition, natural language processing, data mining, pattern recognition, control systems, optimization problems, and healthcare.


The book provides a comprehensive overview of the field of neural networks and their implementation using MATLAB 6.0 and Neural Network Toolbox. The book is well-organized and easy to follow. Each chapter contains theoretical concepts followed by examples and exercises using MATLAB code. The book also includes a supplemental set of MATLAB code files that can be downloaded from the publisher's website.


Introduction to Neural Networks Using MATLAB 6.0 is a useful resource for students who want to learn about neural networks and their applications using MATLAB 6.0 and Neural Network Toolbox. The book can also serve as a reference for researchers and practitioners who work with neural networks in various fields.


The book is divided into 12 chapters, each focusing on a different aspect of neural networks and their applications. The first chapter introduces the basic concepts and terminology of neural networks, such as neurons, synapses, activation functions, learning rules, architectures, and types. The second chapter discusses the perceptron model and its variants, such as the single-layer perceptron, the multilayer perceptron, and the delta rule. The third chapter explains the radial basis function network model and its applications to function approximation and interpolation. The fourth chapter covers the recurrent network model and its applications to time series prediction and dynamic system modeling. The fifth chapter describes the Hopfield network model and its applications to associative memory and optimization problems. The sixth chapter explores the self-organizing map model and its applications to data visualization and clustering. The seventh chapter introduces the learning vector quantization model and its applications to classification and pattern recognition. The eighth chapter presents the adaptive resonance theory model and its a




About

Welcome to the group! You can connect with other members, ge...
bottom of page