Soft Computing : Neuro-Fuzzy and Genetic Algorithms.

Format:

Book

Author/Creator:

Roy, Samir.

Contributor:

Chakraborty, Udit.

Language:

English

Subjects (All):

Soft computing--Problems, exercises, etc.

Soft computing.

Fuzzy logic--Problems, exercises, etc.

Fuzzy logic.

Genetic algorithms--Problems, exercises, etc.

Genetic algorithms.

Genetic programming (Computer science)--Problems, exercises, etc.

Genetic programming (Computer science).

Physical Description:

1 online resource (609 pages)

Edition:

1st ed.

Other Title:

Soft Computing

Place of Publication:

Noida : Pearson India, 2013.

Summary:

Soft computing is a branch of computer science that deals with a family of methods that imitate human intelligence. This is done with the goal of creating tools that will contain some human-like capabilities (such as learning, reasoning and decision-making). This book covers the entire gamut of soft computing, including fuzzy logic, rough sets, artificial neural networks, and various evolutionary algorithms. It offers a learner-centric approach where each new concept is introduced with carefully designed examples/instances to train the learner.

Contents:

Cover

Contents

Preface

Acknowledgements

About the Authors

Chapter 1: Introduction

1.1 What is Soft Computing?

1.2 Fuzzy Systems

1.3 Rough Sets

1.4 Artificial Neural Networks

1.5 Evolutionary Search Strategies

Chapter Summary

Test Your Knowledge

Answers

Exercises

Bibliography and Historical Notes

Chapter 2: Fuzzy Sets

2.1 Crisp Sets: A Review

2.1.1 Basic Concepts

2.1.2 Operations on Sets

2.1.3 Properties of Sets

2.2 Fuzzy Sets

2.2.1 Fuzziness/Vagueness/Inexactness

2.2.2 Set Membership

2.2.3 Fuzzy Sets

2.2.4 Fuzzyness vs. Probability

2.2.5 Features of Fuzzy Sets

2.3 Fuzzy Membership Functions

2.3.1 Some Popular Fuzzy Membership Functions

2.3.2 Transformations

2.3.3 Linguistic Variables

2.4 Operations on Fuzzy Sets

2.5 Fuzzy Relations

2.5.1 Crisp Relations

2.5.2 Fuzzy Relations

2.5.3 Operations on Fuzzy Relations

2.6 Fuzzy Extension Principle

2.6.1 Preliminaries

2.6.2 The Extension Principle

Solved Problems

Chapter 3: Fuzzy Logic

3.1 Crisp Logic: A Review

3.1.1 Propositional Logic

3.1.2 Predicate Logic

3.1.3 Rules of Inference

3.2 Fuzzy Logic Basics

3.2.1 Fuzzy Truth Values

3.3 Fuzzy Truth in Terms of Fuzzy Sets

3.4 Fuzzy Rules

3.4.1 Fuzzy If-Then

3.4.2 Fuzzy If-Then-Else

3.5 Fuzzy Reasoning

3.5.1 Fuzzy Quantifiers

3.5.2 Generalized Modus Ponens

3.5.3 Generalized Modus Tollens

Chapter 4: Fuzzy Inference Systems

Introduction

4.2 Fuzzification of the Input Variables

4.3 Application of Fuzzy Operators on the Antecedent Parts of the Rules.

4.4 Evaluation of the Fuzzy Rules

4.5 Aggregation of Output Fuzzy Sets Across the Rules

4.6 Defuzzification of the Resultant Aggregate Fuzzy Set

4.6.1 Centroid Method

4.6.2 Centre-of-Sums (CoS) Method

4.6.3 Mean-of-Maxima (MoM) Method

4.7 Fuzzy Controllers

4.7.1 Fuzzy Air Conditioner Controller

4.7.2 Fuzzy Cruise Controller

Chapter 5: Rough Sets

5.1 Information Systems and Decision Systems

5.2 Indiscernibility

5.3 Set Approximations

5.4 Properties of Rough Sets

5.5 Rough Membership

5.6 Reducts

Application

Chapter 6: Artificial Neural Networks:Basic Concepts

6.1 Introduction

6.1.1 The Biological Neuron

6.1.2 The Artificial Neuron

6.1.3 Characteristics of the Brain

6.2 Computation in Terms of Patterns

6.2.1 Pattern Classification

6.2.2 Pattern Association

6.3 The McCulloch-Pitts Neural Model

6.4 The Perceptron

6.4.1 The Structure

6.4.2 Linear Separability

6.4.3 The XOR Problem

6.5 Neural Network Architectures

6.5.1 Single Layer Feed Forward ANNs

6.5.2 Multilayer Feed Forward ANNs

6.5.3 Competitive Network

6.5.4 Recurrent Networks

6.6 Activation Functions

6.6.1 Identity Function

6.6.2 Step Function

6.6.3 The Sigmoid Function

6.6.4 Hyperbolic Tangent Function

6.7 Learning by Neural Nets

6.7.1 Supervised Learning

6.7.2 Unsupervised Learning

Chapter 7: Pattern Classifiers

7.1 Hebb Nets

7.2 Perceptrons

7.3 Adaline

7.4 Madaline

Chapter Summary.

Solved Problems

Chapter 8: Pattern Associators

8.1 Auto-associative Nets

8.1.1 Training

8.1.2 Application

8.1.3 Elimination of Self-connection

8.1.4 Recognition of Noisy Patterns

8.1.5 Storage of Multiple Patterns in an Auto-associative Net

8.2 Hetero-associative Nets

8.2.1 Training

8.2.2 Application

8.3 Hopfield Networks

8.3.1 Architecture

8.3.2 Training

8.4 Bidirectional Associative Memory

8.4.1 Architecture

8.4.2 Training

8.4.3 Application

Chapter 9: Competitive Neural Nets

9.1 The Maxnet

9.1.1 Training a MAXNET

9.1.2 Application of Maxnet

9.2 Kohonen's Self-organizing Map (SOM)

9.2.1 SOM Architecture

9.2.2 Learning by Kohonen's SOM

9.2.3 Application

9.3 Learning Vector Quantization (LVQ)

9.3.1 LVQ Learning

9.3.2 Application

9.4 Adaptive Resonance Theory (ART)

9.4.1 The Stability-Plasticity Dilemma

9.4.2 Features of ART Nets

9.4.3 Art 1

Chapter 10: Backpropagation

10.1 Multi-layer Feedforward Net

10.1.1 Architecture

10.1.2 Notational Convention

10.1.3 Activation Functions

10.2 The Generalized Delta Rule

10.3 The Backpropagation Algorithm

10.3.1 Choice of Parameters

10.3.2 Application

Chapter 11: Elementary Search Techniques

11.1 State Spaces

11.2 State Space Search

11.2.1 Basic Graph Search Algorithm

11.2.2 Informed and Uninformed Search

11.3 Exhaustive Search.

11.3.1 Breadth-first Search (BFS)

11.3.2 Depth-first Search (DFS)

11.3.3 Comparison Between BFS and DFS

11.3.4 Depth-first Iterative Deepening

11.3.5 Bidirectional Search

11.3.6 Comparison of Basic Uninformed Search Strategies

11.4 Heuristic Search

11.4.1 Best-first Search

11.4.2 Generalized State Space Search

11.4.3 Hill Climbing

11.4.4 The A/A* Algorithms

11.4.5 Problem Reduction

11.4.6 Means-ends Analysis

11.4.7 Mini-Max Search

11.4.8 Constraint Satisfaction

11.4.9 Measures of Search

11.5 Production Systems

Chapter 12: Advanced Search Strategies

12.1 Natural Evolution: A Brief Review

12.1.1 Chromosomes

12.1.2 Natural Selection

12.1.3 Crossover

12.1.4 Mutation

12.2 Genetic Algorithms (GAs)

12.2.1 Chromosomes

12.2.2 Fitness Function

12.2.3 Population

12.2.4 GA Operators

12.2.5 Elitism

12.2.6 GA Parameters

12.2.7 Convergence

12.3 Multi-objective Genetic Algorithms

12.3.1 MOO Problem Formulation

12.3.2 The Pareto-optimal Front

12.3.3 Pareto-optimal Ranking

12.3.4 Multi-objective Fitness

12.3.5 Multi-objective GA Process

12.4 Simulated Annealing

Exercise

Chapter 13: Hybrid Systems

13.1 Neuro-genetic Systems

13.1.1 GA-based Weight Determination of Multi-layerFeed-forward Net

13.1.2 Neuro-evolution of Augmenting Topologies (NEAT)

13.2 Fuzzy-Neural Systems

13.2.1 Fuzzy Neurons

13.2.2 Adaptive Neuro-fuzzy Inference System (ANFIS)

13.3 Fuzzy-genetic Systems

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Includes index.

ISBN:

93-325-1420-8

OCLC:

883377632