fuzzy logic and neural networks basic concepts and applications ebook by Chennakesava R Alavala free download

E-Book Details:

Title:	Fuzzy Logic and Neural Networks Basic Concepts & Application
Publisher:	New Age International
Author:	Chennakesava R Alavala
Edition:	Paperback 1st
Format:	PDF
ISBN:	8122421828
EAN:	9788122421828
No.ofPages:	276

Book Description:

The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required.
                                        Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank which is intended to help in the preparation for external examination. This book consists of 125 illustrations.

ABOUT THE AUTHOR:

Chennakesava R. Alavala B.E., M.E., M.Tech., Ph.D. (Mech.), Ph.D. (CAD/CAM), is a Professor of Mechanical Engineering, Jawaharlal Nehru Technological University, Hyderabad. The author has published 102 technical papers worldwide. He is the recipient of ?best paper? awards nine times. He has successfully completed several R&D and consultancy projects. He has guided many research scholars for their Ph.D. He is a Governing Body Member for several engineering colleges in Andhra Pradesh.

Table of Contents:

Unit – I: Introduction to Neural Networks

Introduction, Humans and Computers, Organization of the Brain, Biological Neuron, Biological and

Artificial Neuron Models, Hodgkin-Huxley Neuron Model, Integrate-and-Fire Neuron Model, Spiking Neuron Model, Characteristics of ANN, McCulloch-Pitts Model, Historical Developments, Potential Applications of ANN.

Unit- II: Essentials of Artificial Neural Networks

Artificial Neuron Model, Operations of Artificial Neuron, Types of Neuron Activation Function, ANN

Architectures, Classification Taxonomy of ANN – Connectivity, Neural Dynamics (Activation and

Synaptic), Learning Strategy (Supervised, Unsupervised, Reinforcement), Learning Rules, Types of

Application

Unit–III: Single Layer Feed Forward Neural Networks

Introduction, Perceptron Models: Discrete, Continuous and Multi-Category, Training Algorithms: Discrete and Continuous Perceptron Networks, Perceptron Convergence theorem, Limitations of the Perceptron Model, Applications.

Unit- IV: Multilayer Feed forward Neural Networks

Credit Assignment Problem, Generalized Delta Rule, Derivation of Backpropagation (BP) Training,

Summary of Backpropagation Algorithm, Kolmogorov Theorem, Learning Difficulties and Improvements.

Unit V: Associative Memories

Paradigms of Associative Memory, Pattern Mathematics, Hebbian Learning, General Concepts of

Associative Memory (Associative Matrix, Association Rules, Hamming Distance, The Linear Associator, Matrix Memories, Content Addressable Memory), Bidirectional Associative Memory (BAM) Architecture, BAM Training Algorithms: Storage and Recall Algorithm, BAM Energy Function, Proof of BAM Stability Theorem Architecture of Hopfield Network: Discrete and Continuous versions, Storage and Recall Algorithm, Stability Analysis, Capacity of the Hopfield Network

Summary and Discussion of Instance/Memory Based Learning Algorithms, Applications.

Unit – VI: Classical & Fuzzy Sets

Introduction to classical sets - properties, Operations and relations; Fuzzy sets, Membership,

Uncertainty, Operations, properties, fuzzy relations, cardinalities, membership functions.

UNIT VII: Fuzzy Logic System Components

Fuzzification, Membership value assignment, development of rule base and decision making system, Defuzzification to crisp sets, Defuzzification methods.

UNIT VIII: Applications

Neural network applications: Process identification, control, fault diagnosis and load forecasting.

Fuzzy logic applications: Fuzzy logic control and Fuzzy classification.

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