TABLE OF CONTENT

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0. PREFACE

 

1. I NTRODUCTION TO INTELLIGENT SYSTEMS AND SOFT COMPUTING

1. 1 INTRODUCTION

1. 2 INTELLIGENT SYSTEMS

1. 2. 1 Machine Intelligence

1. 2. 2 Meaning of Intelligence

1. 2. 3 Dynamics of Intelligence

1. 2. 4 Intelligent Machines

1. 3 KNOWLEDGE-BASED SYSTEMS

1. 3. 1 Architectures of Knowledge-Based Systems

1. 3. 2 Production Systems

1. 3. 2. 1 Reasoning Strategies

1. 3. 2. 2 Conflict Resolution Methods

1. 3. 3 Frame-Based Systems

1. 3. 4 Blackboard Systems

1. 3. 5 Object-Oriented Programming

1. 3. 6 Expert Systems

1. 3. 6. 1 Development of an Expert System

1. 3. 6. 2 Knowledge Engineering

1. 3. 6. 3 Applications

1. 4 KNOWLEDGE REPRESENTATION AND PROCESSING

1. 4. 1 Semantic Networks

1. 4. 2 Crisp Logic

1. 4. 2. 1 Crisp Sets

1. 4. 2. 2 Operations of Sets

1. 4. 2. 3 Logic

1. 4. 2. 4 Correspondence Between Sets and Logic

1. 4. 2. 5 Logic Processing (Reasoning and Inference)

1. 4. 2. 6 Laws of Logic

1. 4. 2. 7 Rules of Inference

1. 4. 2. 8 Propositional Calculus and Predicate Calculus

1. 5 SOFT COMPUTING

1. 5. 1 Fuzzy Logic

1. 5. 2 Neural Networks

1. 5. 3 Genetic Algorithms

1. 5. 4 Probabilistic Reasoning

1. 5. 5 Approximation and Intelligence

1. 5. 6 Technology Needs

PROBLEMS

REFERENCES

 

2. FUNDAMENTALS OF FUZZY LOGIC SYSTEMS

2. 1 INTRODUCTION

2. 2 BACKGROUND

2. 2. 1 Evolution of Fuzzy Logic

2. 2. 1. 1 Popular Applications

2. 2. 2 Stages of Development of an Intelligent Product

2. 2. 3 Use of Fuzzy Logic in Expert Systems

2. 3 FUZZY SETS

2. 3. 1 Membership Function

2. 3. 2 Symbolic Representation

2. 4 FUZZY LOGIC OPERATIONS

2. 4. 1 Complement (Negation, NOT)

2. 4. 2 Union (Disjunction, OR)

2. 4. 3 Intersection (Conjunction, AND)

2. 4. 4 Basic Laws of Fuzzy Logic

2. 5 GENERALIZED FUZZY OPERATIONS

2. 5. 1 Generalized Fuzzy Complement

2. 5. 2 Triangular Norms

2. 5. 2. 1 T-Norm (Generalized Intersection)

2. 5. 2. 2 S-Norm or Triangular Conorm (Generalize Union)

2. 5. 3 Set Inclusion (AB)

2. 5. 3. 1 Grade of Inclusion

2. 5. 4 Set Equality (A = B)

2. 5. 4. 1 Grade of Equality

2. 6 IMPLICATION (IF-THEN)

2. 6. 1 Considerations of Fuzzy Implication

2. 7 SOME DEFINITIONS

2. 7. 1 Height of a Fuzzy Set

2. 7. 2 Support Set

2. 7. 3 -Cut of a Fuzzy Set

2. 7. 4 Representation Theorem

2. 8 FUZZINESS AND FUZZY RESOLUTION

2. 8. 1 Fuzzy Resolution

2. 8. 2 Degree of Fuzziness

2. 8. 2. 1 Measures of Fuzziness

2. 9 FUZZY RELATIONS

2. 9. 1 Analytical Representation of a Fuzzy Relation

2. 9. 2 Cartesian Product of Fuzzy Sets

2. 9. 3 Extension Principle

2. 10 COMPOSITION AND INFERENCE

2. 10. 1 Projection

2. 10. 2 Cylindrical Extension

2. 10. 3 Join

2. 10. 4 Composition

2. 10. 4. 1 Sup-Product Composition

2. 10. 5 Compositional Rule of Inference

2. 10. 5. 1 Composition Through Matrix Multiplication

2. 10. 6 Properties of Composition

2. 10. 6. 1 Sup-t Composition

2. 10. 6. 2 Inf-s Composition

2. 10. 6. 3 Commutativity

2. 10. 6. 4 Associativity

2. 10. 6. 5 Distributivity

2. 10. 6. 6 DeMorgans Law

2. 10. 6. 7 Inclusion

2. 10. 7 Extension Principle

2. 11 Considerations of Fuzzy Decision Making

2. 11. 1 Extensions to Fuzzy Decision Making

PROBLEMS

REFERENCES

 

3. FUZZY LOGIC CONTROL

3. 1 INTRODUCTION

3. 2 BACKGROUND

3. 3 BASICS OF FUZZY CONTROL

3. 3. 1 Steps of Fuzzy Logic Control

3. 3. 2 Composition Using Individual Rules

3. 3. 3 Defuzzification

3. 3. 3. 1 Centroid Method

3. 3. 3. 2 Mean of Maxima Method

3. 3. 3. 3 Threshold Methods

3. 3. 3. 4 Comparison of the Defuzzification Methods

3. 3. 4 Fuzzification

3. 3. 4. 1 Singleton Method

3. 3. 4. 2 Triangular Function Method

3. 3. 4. 3 Gaussian Function Method

3. 3. 4. 4 Discrete Case of Fuzzification

3. 3. 5 Fuzzy Control Surface

3. 3. 6 Extensions of Mamdani Fuzzy Control

3. 4 FUZZY CONTROL ARCHITECTURES

3. 4. 1 Hierarchical Fuzzy Systems

3. 4. 2 Hierarchical Model

3. 4. 2. 1 Feedback/Filter Modules

3. 4. 2. 2 Functional/Control Modules

3. 4. 3 Effect of Information Processing

3. 4. 4 Effect of Signal Combination on Fuzziness

3. 4. 5 Decision Table Approach for a Fuzzy Tuner

3. 5 PROPERTIES OF FUZZY CONTROL

3. 5. 1 Fuzzy Controller Requirements

3. 5. 2 Completeness

3. 5. 3 Continuity

3. 5. 4 Consistency

3. 5. 5 Rule Validity

3. 5. 6 Rule Interaction

3. 5. 7 Rule-Base Decoupling

3. 5. 7. 1 Decision Making through a Coupled Rule-base

3. 5. 7. 2 Decision Making through an Uncoupled Rule-base

3. 5. 7. 3 Equivalence Condition

3. 6 ROBUSTNESS AND STABILITY

3. 6. 1 Fuzzy Dynamic Systems

3. 6. 2 Stability of Fuzzy Systems

3. 6. 2. 1 Traditional Approach to Stability Analysis

3. 6. 2. 2 Composition Approach to Stability Analysis

3. 6. 3 Eigen-fuzzy Sets

3. 6. 3. 1 Iterative Method

PROBLEMS

REFERENCES

 

 

4. FUNDAMENTALS OF NEURAL NETWORKS

4. 1 INTRODUCTION

4. 2 LEARNING AND ACQUISITION OF KNOWLEDGE

4. 2. 1 Symbolic Learning

4. 2. 2 Numerical Learning

4. 3 FEATURES OF ARTIFICIAL NEURAL NETWORKS

4. 3. 1 Neural Network Topologies

4. 3. 1. 1 The Feedforward Topology

4. 3. 1. 2 The Recurrent Topology

4. 3. 2 Neural Network Activation Functions

4. 3. 3 Neural Network Learning Algorithms

4. 3. 3. 1 Supervised Learning

4. 3. 3. 2 Unsupervised Learning

4. 3. 3. 3 Reinforcement Learning

4. 4 FUNDAMENTALS OF CONNECTIONIST MODELING

4. 4. 1 McCulloch-Pitts Models

4. 4. 2 Perceptron

4. 4. 3 Adaline

4. 4. 4 Madaline

4. 5 SUMMARY

PROBLEMS

REFERENCES

 

 

5. MAJOR CLASSES OF NEURAL NETWORKS

5. 1 INTRODUCTION

5. 2 The Multilayer Perceptron

5. 2. 1 Topology

5. 2. 2 Backpropagation Learning Algorithm

5. 2. 3 Momentum

5. 2. 4 Applications and limitations of MLP

5. 3 RADIAL BASIS FUNCTION NETWORKS

5. 3. 1 Topology

5. 3. 2 Learning algorithm for RBF

5. 3. 3 Applications

5. 4 KOHONENS SELF-ORGANIZING NETWORK

5. 4. 1 Topology

5. 4. 2 Learning Algorithm

5. 4. 3 Applications

5. 5 HOPEFIELD NETWORK

5. 5. 1 Topology

5. 5. 2 Learning Algorithm

5. 5. 3 Applications of Hopefield Networks

5. 6 INDUSTRIAL AND COMMERCIAL APPLICATIONS OF ANN

5. 6. 1 Neural Networks for Process Monitoring and Optimal Control

5. 6. 2 Neural Networks in Semiconductor Manufacturing Processes

5. 6. 3 Neural Networks for Power Systems

5. 6. 4 Neural Networks in Robotics

5. 6. 5 Neural Networks in Communications

5. 6. 6 Neural Networks in Decision Fusion and Pattern Recognition

5. 7 CONCLUSION

PROBLEMS

REFERENCES

 

6. DYNAMIC NEURAL NETWORKS AND THEIR APPLICATIONS TO CONTROL AND CHAOS PREDICTION

6. 1 INTRODUCTION

6. 2 BACKGROUND

6. 2. 1 Basic Concepts of Recurrent Networks

6. 2. 2 The Dynamics of Recurrent Neural Networks

6. 2. 3 Architecture

6. 3 TRAINING ALGORITHMS

6. 3. 1 Back-Propagation Through Time (BPTT)

6. 3. 2 Real Time Backpropagation Learning

6. 4 FIELDS OF APPLICATIONS OF RNN

6. 5 DYNAMIC NEURAL NETWORKS FOR IDENTIFICATION AND CONTROL

6. 5. 1 Background

6. 5. 2 Conventional Approaches for Identification and Control

6. 5. 2. 1 Systems Identification

6. 5. 2. 2 Adaptive Control

6. 6 NEURAL NETWORK BASED CONTROL APPROACHES

6. 6. 1 Neural Networks for Identification

6. 6. 2 Neural Networks for Control

6. 6. 2. 1 Supervised Control

6. 6. 2. 2 Inverse Control

6. 6. 2. 3 Neuro Adaptive Control

6. 7 DYNAMIC NEURAL NETWORKS FOR CHAOS TIME SERIES PREDICTION

6. 7. 1 Background

6. 7. 2 Conventional Techniques for Chaotic Systems Prediction and Control

6. 7. 3 Artificial Neural Networks for Chaos Prediction

6. 7. 3. 1 Conventional Feedforward Networks

6. 7. 3. 2 Recurrent Neural Networks (RNNs) Based Predictors

6. 8 CONCLUSION

REFERENCES

 

7. NEURO FUZZY SYSTEMS

7. 1 INTRODUCTION

7. 2 BACKGROUND

7. 3 ARCHITECTURES OF NEURO-FUZZY SYSTEMS

7. 3. 1 Cooperative Neuro-Fuzzy Systems

7. 3. 1. 1 Neural networks for determining membership functions

7. 3. 1. 2 Adeli-Hung algorithm (AHA)

7. 3. 1. 3 Learning fuzzy rules using neural networks

7. 3. 1. 4 Learning in fuzzy systems using neural networks

7. 3. 1. 5 Identifying weighted fuzzy rules using neural networks

7. 3. 2 Neural Network-Driven Fuzzy Reasoning

7. 3. 3 Hybrid Neuro-Fuzzy Systems

7. 3. 3. 1 Architecture of hybrid neuro-fuzzy systems

7. 3. 3. 2 Five-layer neuro-fuzzy systems (ANFIS)

7. 3. 3. 3 Four-layer neuro-fuzzy systems (ANFIS)

7. 3. 3. 4 Three-layer neuro-fuzzy approximator

7. 4 CONSTRUCTION OF NEURO-FUZZY SYSTEMS

7. 4. 1 Structure identification phase

7. 4. 1. 1 Grid-type partitioning

7. 4. 1. 2 Clustering

7. 4. 1. 3 Scatter partitioning

7. 4. 2 Parameter Learning Phase

7. 4. 2. 1 The Backpropagation learning algorithm

7. 4. 2. 2 Hybrid learning algorithms

7. 5 CONCLUDING REMARKS

PROBLEMS

REFERENCES

 

 

8. EVOLUTIONARY COMPUTING

8. 1 INTRODUCTION

8. 2 OVERVIEW ON EVOLUTIONARY COMPUTING

8. 2. 1 Evolutionary Programming

8. 2. 2 Evolutionary Strategies

8. 2. 3 Genetic Programming

8. 2. 4 Genetic Algorithms

8. 3 GENETIC ALGORITHMS AND OPTIMIZATION

8. 3. 1 Genotype

8. 3. 2 Fitness Function

8. 4 THE SCHEMA THEOREM: THE FUNDAMENTAL THEOREM OF GENETIC ALGORITHMS

8. 5 GENETIC ALGORITHMS OPERATORS

8. 5. 1 Selection

8. 5. 2 Crossover

8. 5. 3 Mutation

8. 5. 4 Mode of operation of GAs

8. 5. 5 Steps for implementing GAs

8. 5. 6 Search Process in GAs

8. 6 INTEGRATION OF GENETIC ALGORITHMS WITH NEURAL NETWORKS

8. 6. 1 Use of GAs for ANN Input Selection

8. 6. 2 Using GA for NN learning

8. 7 INTEGRATION OF GENETIC ALGORITHMS WITH FUZZY LOGIC

8. 8 KNOWN ISSUES IN GAS

8. 8. 1 Local Minima and premature Convergence

8. 8. 2 Mutation Interference

8. 8. 3 Deception

8. 8. 4 Epistasis

8. 9 POPULATION-BASED INCREMENTAL LEARNING

8. 9. 1 Basics of PBIL

8. 9. 2 Generating the Population

8. 9. 3 PBIL Algorithm

8. 9. 4 PBIL and Learning Rate

8. 10 EVOLUTIONARY STRATEGIES

8. 11 ES APPLICATIONS

8. 11. 1 Parameter estimation

8. 11. 2 Image Processing and Computer Vision Systems

8. 11. 3 Task scheduling by ES

8. 11. 4 Mobile manipulator path planning by ES

8. 11. 5 Car automation using ES

8. 12 CONCLUSION

PROBLEMS

REFERENCES

9. SOFT COMPUTING FOR SMART MACHINE DESIGN

9. 1 INTRODUCTION

9. 1. 1 Intelligent Machines

9. 1. 2 Intelligent Control

9. 1. 3 Hierarchical Architecture

9. 1. 4 Development Steps

9. 2 CONTROLLER TUNING

9. 2. 1 Problem Formulation

9. 2. 1. 1 Rule-Base

9. 2. 1. 2 Compositional Rule of Inference

9. 2. 2 Tuning Procedure

9. 2. 2. 1 Rule Dissociation

9. 2. 2. 2 Resolution Relations

9. 2. 2. 3 Tuning Inference

9. 2. 2. 4 Accuracy Versus Fuzzy Resolution

9. 2. 3 Illustrative Example

9. 2. 3. 1 Resolution Relation

9. 2. 3. 2 Stability Region

9. 2. 3. 3 Tuning Results

9. 3 SUPERVISORY CONTROL OF A FISH PROCESSING MACHINE

9. 3. 1 Machine Features

9. 3. 2 Supervisory Control System

9. 3. 3 Information Preprocessing

9. 3. 3. 1 Image Preprocessing

9. 3. 3. 2 Servomotor Response Preprocessing

9. 3. 3. 3 Cutter Load Preprocessing

9. 3. 3. 4 Conveyor Speed Preprocessing

9. 3. 4 Knowledge-Based Decision Making

9. 3. 4. 1 Knowledge Acquisition

9. 3. 4. 2 Decision Making

9. 3. 4. 3 Servo Tuning

9. 3. 4. 4 Product Quality Assessment

9. 3. 4. 5 Machine Tuning

9. 3. 5 System Implementation

9. 3. 5. 1 System Modules

9. 3. 5. 2 User Interface of the Machine

9. 3. 6 Performance Testing

9. 3. 6. 1 Servomotor Tuning Examples

9. 3. 6. 2 Machine Tuning Example

9. 3. 6. 3 Product Quality Assessment Example

9. 4 PROBLEMS

9. 5 REFERENCES

 

10. TOOLS OF SOFT COMPUTING IN REAL WORLD APPLICATIONS

10. 1 Case Study 1: Expert Parameter Tuning of DC Motor Controller

10. 2 Case Study 2: Stabilizing Control of a High-Order Power System by Neural Adaptive

Feedback Linearization

10. 3 Case Study 3: Soft Computing Tools For Solving a Class of Facilities Layout Planning

Problem

10. 4 Case Study 4: Mobile Position Estimation Using an RBF Network in CDMA Cellular

Systems