Neural networks, fuzzy logic. and genetic algorithms: systhesis and applications (Record no. 3342)
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000 -LEADER | |
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fixed length control field | 10645nam a22001817a 4500 |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9788120321861 |
040 ## - CATALOGING SOURCE | |
Transcribing agency | CUS |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.32 |
Item number | RAJ/N |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | Rajasekaran, S. |
245 ## - TITLE STATEMENT | |
Title | Neural networks, fuzzy logic. and genetic algorithms: systhesis and applications |
Statement of responsibility, etc. | S. Rajasekaran and G. A. Vijayalakshmi Pai |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication, distribution, etc. | Delhi: |
Name of publisher, distributor, etc. | PHI Learning Private Limited, |
Date of publication, distribution, etc. | 2014. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | xvi, 439 p. |
505 ## - FORMATTED CONTENTS NOTE | |
Formatted contents note | 1 INTRODUCTION TO ARTIFICIAL INTELLIGENCE SYSTEMS<br/>1.1 Neural Networks 2<br/>1.2 Fuzzy Logic 3<br/>1.3 Genetic Algorithms 4<br/>1.4 Structiire of This book 5<br/>Summary 6<br/>References 6<br/>Part 1 NEURAL NETWORKS<br/>2 FUNDAMENTALS OF NEURAL NETWORKS<br/>2.1 Basic Concepts of Neural Networks II<br/>2.2 Human Brain 12<br/>2.3 Model of an Artificial Neuron 13<br/>2.4 Neural Network Architectures 16<br/>2.4.1 Single Layer Feedforward Network 17<br/>2.4.2 Multilayer Feedforward Network 18<br/>2.4.3 Recurrent Networks 18<br/>2.5 Characteristics of Neural Networks 19<br/>2.6 Learning Methods 19<br/>2.7 Taxonomy of Neural Network Architectures 21<br/>2.8 History of Neural Network Research 22<br/>2.9 Early Neural Network Architectures 23<br/>2.9.1 Rosenblatt's Perceptron 23<br/>2.9.2 ADALINE Network 28<br/>2.9.3 MADALINE Network 28<br/>2.10 Some Application Domains 30<br/>Summary 31<br/>Programming Assignment 31<br/>Suggested Further Reading 32<br/>References 32<br/>3 BACKFROPAGATION NETWORKS<br/>3.1 Architecture of a Backpropagation Network 35<br/>3.1.1 The Perceptron Model 35<br/>3.1.2 The Solution 36<br/>3.1.3 Single Layer Artificial Neural Network 39<br/>3.1.4 Model for Multilayer Perceptron 41<br/>3.2 Backpropagation Learning 42<br/>3.2.1 Input Layer Computation 43<br/>3.2.2 Hidden Layer Computation 43<br/>3.2.3 Output Layer Computation 44<br/>3.2.4 Calculation of Error 45<br/>3.2.5 Training of Neural Network 45<br/>3.2.6 Method of Steepest Descent 47<br/>3.2.7 Effect of Learning Rate *7]' 51<br/>3.2.8 Adding a Momentum Term 52<br/>3.2.9 Backpropagation Algorithm 53<br/>3.3 Illustration 56<br/>3.4 Applications 59<br/>3.4.1 Design of Journal Bearing 59<br/>3.4.2 Classification of Soil 64<br/>3.4.3 Hot Extrusion of Steel 65 .<br/>3.5 Effect of Tuning Parameters of the Backpropagation Neural Network<br/>3.6 Selection of Various Parameters in BPN 72<br/>3.6.1 Number of Hidden Nodes 72<br/>3.6.2 Momentum Coefficient a 73<br/>3.6.3 Sigmoidal Gain A 74<br/>3.6.4 Local Minima 74<br/>3.6.5 Learning Coefficient T] 74<br/>3.7 Variations of Standard Backpropagation Algorithm 75<br/>3.7.1 Decremental Iteration Procedure 75<br/>3.7.2 Adaptive Backpropagation (Accelerated Learning) 7o<br/>3.7.3 Genetic Algorithm Based Backpropagation 77<br/>3.7.4 Quick Prop Training 77<br/>3.7.5 Augmented BP Networks 77 , xt i<br/>3.7.6 Sequential Learning Approach for Single Hidden Layer Neura<br/>Networks 80<br/>3.8 Research Directions 80<br/>3.8.1 New Topologies 80<br/>3.8.2 Better Learning Algorithms 81<br/>3.8.3 Better Training Strategies 81<br/>3.8.4 Hardware Implementation 81<br/>3.8.5 Conscious Networks 81<br/>Summary 82<br/>Programming Assignment 82<br/>References 85<br/>associative memory<br/>41 Autocorrelators<br/>42 Heterocorrelators: Kosko's Discrete BAM 91<br/>4.3<br/>42 1 Addition and Deletion of Pattern Pairs 91<br/>4 2 1 . —<br/>422 Energy Function for BAM 92<br/>Wang et al.'s Multiple Training Encoding Strategy 95<br/>4.4 Exponential BAM 99<br/>4.4.1 Evolution Equations 99<br/>4.5 Associative Memory for Real-coded Pattern Pairs 101<br/>4.5.1 Input Normalization 101<br/>4.5.2 Evolution Equations 101<br/>4.6 Applications 105<br/>4.6.1 Recognition of Characters 105<br/>4.6.2 Fabric Defect Identification 107<br/>4.7 Recent Trends 110<br/>Summary 111<br/>Programming Assignment 111<br/>References 114<br/>ADAPTIVE RESONANCE THEORY<br/>5.1 Introduction 117<br/>5.1.1 Cluster Structure 117<br/>5.1.2 Vector Quantization 118<br/>5.1.3 Classical ART Networks 125<br/>5.1.4' Simplified ART Architecture 126<br/>5.2 ARTl 127<br/>5.2.1 Architecture of ARTl 128<br/>5.2.2 Special Features of ARTl Models 129<br/>5.2.3 ARTl Algorithm 133<br/>5.2.4 Illustration 135<br/>5.3 ART2 140<br/>5.3.1 Architecture of ART2 140<br/>5.3.2 ART2 Algorithm 141<br/>5.3.3 Illustration 144<br/>5.4 Applications 145<br/>5.4.1 Character Recognition Using ARTl 145<br/>5.4.2 Classification of Soil 146<br/>5.4.3 Prediction of Load from Yield Line Patterns of Elastic-Plastic<br/>Clamped Square Plate 147<br/>5.4.4 Chinese Character Recognition—Some Remarks 151<br/>5.5 Sensitivities of Ordering of Data 151<br/>Summary 152<br/>Programming Assignment 153<br/>Suggested Further Reading 153<br/>References 153<br/>FUZZY SET THEORY<br/>6.1 Fuzzy versus Crisp 157<br/>6.2 Crisp sets 159<br/>Part 2 FUZZY LOGIC<br/>6.2.1 Operations on Crisp Sets 161<br/>6.2.2 Properties of Crisp Sets 163<br/>6.2.3 Partition and Covering 166<br/>6.3 Fuzzy Sets 168<br/>6.3.1 Membership Function 169<br/>6.3.2 Basic Fuzzy Set Operations 171<br/>6.3.3 Properties of Fuzzy Sets 176<br/>6.4 Crisp Relations 179<br/>6.4.1 Cartesian Product 179<br/>6.4.2 Other Crisp Relations 180<br/>6.4.3 Operations on Relations 180<br/>6.5 Fuzzy Relations 182<br/>6.5.1 Fuzzy Cartesian Product 182<br/>6.5.2 Operations on Fuzzy Relations 183<br/>Summary 185<br/>Programming Assignment 186<br/>Suggested Further Reading 186<br/>Reference 186<br/>7 FUZZY SYSTEMS<br/>7.1 Crisp Logic 187<br/>7.1.1 Laws of Propositional Logic 189<br/>7.1.2 Inference in Propositional Logic 191<br/>7.2 Predicate Logic 193<br/>7.2.1 Interpretations of Predicate Logic Formula 195<br/>7.2.2 Inference in Predicate Logic 196<br/>7.3 Fuzzy Logic 198<br/>7.3.1 Fuzzy Quantifiers 201<br/>7.3.2 Fuzzy Inference 202<br/>lA Fuzzy Rule based System 204<br/>7.5 Defuzziflcation Methods 205<br/>7.6 Applications 210<br/>7.6.1 Greg Viot's Fuzzy Cruise Controller 210<br/>7.6.2 Air Conditioner Controller 216<br/>Summary 219<br/>Programming Assignment 220<br/>Suggested Further Reading 220<br/>References 220<br/>Part 3 GENETIC ALGORITHMS<br/>8 fundamentals OF GENETIC ALGORITHMS<br/>8.1 Genetic Algorithms: History 227<br/>8.2 Basic Concepts ^228<br/>8.2.1 Biological Background 228<br/>8.3 Creation of Offsprings 229<br/>8.3.1 Search Space<br/>8.4 Working Principle 230<br/>8.5 Encoding 230<br/>8.5.1 Binary Encoding 231<br/>8.5.2 Octal Encoding (0 to 7) 234<br/>8.5.3 Hexadecimal Encoding (0123456789ABCDEF) 234<br/>8.5.4 Permutation Encoding 235<br/>8.5.5 Value Encoding 236<br/>8.5.6 Tree Encoding 236<br/>8.6 Fitness Function 237<br/>8.7 Reproduction 242<br/>8.7.1 Roulette-wheel Selection 242<br/>8.7.2 Boltzmann Selection 245<br/>8.7.3 Tournament Selection 245<br/>8.7.4 Rank Selection 247<br/>8.7.5 Steady-state Selection 248<br/>8.7.6 Elitism 248<br/>8.7.7 Generation Gap and Steady-state Replacement 249<br/>Summary 250<br/>Programming Assignment 251<br/>References 252<br/>9 GENETIC MODELLING<br/>9.1 Inheritance Operators 253<br/>9.2 Cross Over 254<br/>9.2.1 Single-site Cross Over 254<br/>9.2.2 Two-point Cross Over 255<br/>9.2.3 Multi-point Cross Over 255<br/>9.2.4 Uniform Cross Over 256<br/>9.2.5 Matrix Cross Over (Two-dimensional Cross Over) 257<br/>9.2.6 Cross Over Rate 258<br/>9.3 Inversion and Deletion 259<br/>9.3.1 Inversion 259<br/>9.3.2 Deletion and Duplication 259<br/>9.3.3 Deletion and Regeneration 260<br/>9.3.4 Segregation 260<br/>9.3.5 Cross Over and Inversion 261<br/>9.4 Mutation Operator 261<br/>9.4.1 Mutation 261<br/>9.4.2 Mutation Rate P„ 262<br/>9.5 Bit-wise Operators 265<br/>9.5.1 One's Complement Operator 263<br/>9.5.2 Logical Bit-wise Operators 263<br/>9.5.3 Shift Operators 264<br/>9.6 Bit-wise Operators Used in GA 265<br/>9.7 Generational Cycle 265<br/>9.8 Convergence of Genetic Algorithm 271<br/>9.9 Applications 272<br/>9.9.1 Composite Laminates 272<br/>9.9.2 Constrained Optimization 277<br/>9.10 Multi-level Optimization 283<br/>9.11 Real Life Problem 284<br/>9.12 Differences and Similarities between GA and Other Traditional<br/>Methods 287<br/>9.13 Advances in GA 288<br/>Summary 291<br/>Programming Assignment 292<br/>Suggested Further Reading 292<br/>Some Useful Websites 293<br/>References 293<br/>Part 4 HYBRID SYSTEMS<br/>10 INTEGRATION OF NEURAL NETWORKS, FUZZY LOGIC, AND GENETIC<br/>ALGORITHMS<br/>10.1 Hybrid Systems 298<br/>10.1.1 Sequential Hybrid Systems 298<br/>10.1.2 Auxiliary Hybrid Systems 298<br/>10.1.3 Embedded Hybrid Systems 299<br/>10.2 Neural Networks, Fuzzy Logic, and Genetic Algorithms Hybrids 300<br/>10.2.1 Neuro-Fuzzy Hybrids 300<br/>10.2.2 Neuro-Genetic Hybrids 300<br/>10.2.3 Fuzzy-Genetic Hybrids 301<br/>10.3 Preview of the Hybrid Systems to be Discussed 301<br/>10.3.1 Genetic Algorithm based Backpropagation Network 302<br/>10.3.2 Fuzzy-Backpropagation Network 302<br/>10.3.3 Simplified Fuzzy ARTMAP 302<br/>10.3.4 Fuzzy Associative Memories 302<br/>10.3.5 Fuzzy Logic Controlled Genetic Algorithms 303<br/>Summary 303<br/>References 303<br/>11 genetic ALGORITHM BASED BACKPROPAGATION NETWORKS<br/>41.1 GA Based Weight Determination 306<br/>11.1.1 Coding 306<br/>11.1.2 Weight Extraction 308<br/>11.1.3 Fitness Function 309<br/>11.1.4 Reproduction 311<br/>If. 1.5 Convergence 313<br/>11.2 Applications 322<br/>11.2.1 K-factor Determination in Columns 322<br/>11.2.2 Electrical Load Forecasting 323<br/>Summary 325<br/>Programming Assignment 326<br/>Suggested Further Reading 326<br/>References 327<br/>12 FUZZY BACKPROPAGATION NETWORKS<br/>12.1 LR-type Fuzzy numbers 328<br/>12.1.1 Operations on LR-type Fuzzy Numbers 330<br/>12.2 Fuzzy Neuron 330<br/>12.3 Fuzzy BP Architecture 331<br/>12.4 Learning in Fuzzy BP 333<br/>12.5 Inference by Fuzzy BP 339<br/>12.6 Applications 347<br/>12.6.1 Knowledge Base Evaluation 348<br/>12.6.2 Earthquake damage Evaluation 353<br/>Summary 355<br/>Programming Assignment 356<br/>References 357<br/>13 SIMPLIFIED FUZZY ARTMAP<br/>13.1 Fuzzy ARTMAP: A Brief Introduction 358<br/>13.2 Simplified Fuzzy ARTMAP 359<br/>13.2.1 Input Normalization 360<br/>13.2.2 Output Node Activation 361<br/>13.3 Working of Simplified Fuzzy ARTMAP 364<br/>13.4 Application: Image Recognition 370<br/>13.4.1 Feature Extraction—Moment Based Invariants 372<br/>13.4.2 Computation of Invariants 375<br/>13.4.3 Structure of the Simplified Fuzzy ARTMAP Based<br/>Pattern Recognizer 380<br/>13.4.4 Experimental Study 381<br/>13.5 Recent Trends 384<br/>Summary 384<br/>Programming Assignment 385<br/>References 387<br/>14 FUZZY ASSOCIATIVE MEMORIES<br/>14.1 FAM—An Introduction 389<br/>14.2 Single Association FAM 390<br/>14.2.1 Graphical Method of Inference 392<br/>14.2.2 Correlation Matrix Encoding 393<br/>14.3 Fuzzy Hebb FAMs 395<br/>14.4 FAM Involving a Rule Base 400<br/>14.5 FAM Rules with Multiple Antecedents/Consequents 401<br/>14.5.1 Decomposition Rules 404<br/>14.6 Applications 406<br/>14.6.1 Balancing an Inverted Pendulum 406<br/>14.6.2 Fuzzy Truck Backer-upper System 411<br/>Summary 414<br/>Programming Assignment 415<br/>Suggested Further Reading 416<br/>References 416<br/>15 FUZZY LOGIC CONTROLLED GENETIC ALGORITHMS<br/>15.1 Soft Computing Tools 417<br/>15.1.1 Fuzzy Logic as a Soft Computing Tool 417<br/>15.1.2 Genetic Algorithm as a Soft Computing Tool 418<br/>15.2 Problem Description of Optimum Design 418<br/>15.3 Fuzzy Constraints 419<br/>15.4 Illustrations 420<br/>15.4.1 Optimization of the Weight of a Beam 420<br/>15.4.2 Optimal Mix Design for High Performance Concrete 422<br/>15.5 GA in Fuzzy Logic Controller Design 424<br/>15.6 Fuzzy Logic Controller 425<br/>15.6.1 Components of a Fuzzy Logic Controller (FLC) 425<br/>15.6.2 Fuzzy IF-THEN Rules 426<br/>15.7 FLC-GA Based Structural Optimization 429<br/>15.8 Applications 429<br/>15.8.1 Optimum Truss 429<br/>15.8.2 112 Bar Dome Space Truss 431<br/>Summary 432<br/>Programming Assignment 433<br/>Suggested Further Reading 435<br/>References 435<br/> |
650 ## - SUBJECT | |
Keyword | Neural network |
650 ## - SUBJECT | |
Keyword | Fuzzy logic |
650 ## - SUBJECT | |
Keyword | Computer algorithms |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | GN Books |
Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Full call number | Accession number | Date last seen | Date last checked out | Koha item type |
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Central Library, Sikkim University | Central Library, Sikkim University | General Book Section | 22/06/2016 | 006.32 RAJ/N | P35935 | 02/03/2023 | 02/02/2023 | General Books |