Introduction to neural networks, fuzzy logic & genetic algorithms: theory and applications/ (Record no. 3386)
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000 -LEADER | |
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fixed length control field | 04407nam a2200181 4500 |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9788184950793 |
040 ## - CATALOGING SOURCE | |
Transcribing agency | CUS |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.32 |
Item number | VAL/I |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | Valluru, Sudarshan K. |
245 ## - TITLE STATEMENT | |
Title | Introduction to neural networks, fuzzy logic & genetic algorithms: theory and applications/ |
Statement of responsibility, etc. | Sudarshan K. Valluru and T. Nageswara Rao |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication, distribution, etc. | Ahmedabad : |
Name of publisher, distributor, etc. | Jaico Publishing House, |
Date of publication, distribution, etc. | 2011. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | xvi,232p. : |
Other physical details | ill. ; |
Dimensions | 23cm. |
504 ## - BIBLIOGRAPHY, ETC. NOTE | |
Bibliography, etc | Include bibliography |
505 ## - FORMATTED CONTENTS NOTE | |
Formatted contents note | 1. INTRODUCTION TO NEURAL NETWORKS<br/>1.1 Introduction 2<br/>1.2 Humcins Versus Computers 2<br/>1.3 Structure of the Brain .2<br/>1.4 Organization of the Brain 4<br/>1.5 Learning Methodologies in Biological Systems 4<br/>1.6 Basic Neuron Modeling 4<br/>1.7 Computer Versus Biological Neural System 6<br/>1.8 Artificial Neuron Modeling 7<br/>1.9 Characteristics of Artificial Neural Networks 12<br/>1.10 Historical Developments 13<br/>1.11 Potential Applications of Neural Networks 14<br/>1.12 Points to Remember 14<br/>Questions 16<br/>2. ESSENTIALS OF ARTIFICIAL NEURAL NETWORKS<br/>2.1 Introduction 18<br/>2.2 Neural Information Processing 18<br/>2.3 Model of an Artificial Neuron 19<br/>2.4 Operation of an Artificial Neuron and Models of Static and Dynamic<br/>Artificial Neurons (Adaptive Function Estimators) 19<br/>2.5 Neuron Activation Fimctions 21<br/>2.6 Artificial Neural Network Architectures 23<br/>2.7 Taxonomy of an ANN 25<br/>2.8 Neural Djmamics 26<br/>2.9 T)rpes of Applications 31<br/>2.10 Points to Remember 32<br/>Questions 33<br/>3. SINGLE LAYER FEED-FORWARD NEURAL NETWORKS<br/>3.1 Introduction 36<br/>3.2 Generalized Perceptron Model 36<br/>3.3 Perceptron Convergence Theorem 38<br/>3.4 Discrete Single Layer Perceptron 41<br/>3.5 Discrete Single Layer Perceptron Training Algorithm 44<br/>3.6 Continuous Single Layer Perceptron Artificial Neuron Modeling 45<br/>3.7 Continuous Single Layer Perception Training Algorithm 47<br/>3.8 Multi-category Single Layer Perception 47<br/>3.9 Multi-category Single Layer Perception Training Algorithm 51<br/>3.10 Limitations<br/>3.11 Points to Remember<br/>Questions<br/>4. MULTILAYER FEED FORWARD NEURAL NETWORKS<br/>4.1 Introduction<br/>4.2 Credit Assignment Problem<br/>4.3 The New Model<br/>4.4 The Generalized Delta Rule<br/>4.5 Derivation of the Back Propagation (BP) Training Algorithm 60<br/>4.6 Summary of the Back Propagation Algorithm 63<br/>4.7 Kolmogorov's Theorem ^<br/>4.8 Learning Difficulties<br/>4.9 Applications<br/>4.10 Points to Remember<br/>Questions<br/>5. ASSOCIATIVE MEMORIES<br/>5.1 Introduction<br/>5.2 Paradigms of Associative Memory<br/>5.3 Pattern Mathematics<br/>5.4 Hebbian Learning<br/>5.5 General Concepts of Associative Memory<br/>5.6 Bidirectional Associative Memories<br/>5.7 Architecture of a Hopfield Network<br/>5.8 Points to Remember<br/>Questions<br/>6. CLASSICAL AND FUZZY SETS<br/>6.1 Introduction<br/>6.2 The Need for a Fuzzy Theory and its Advantages<br/>6.3 Classical Sets (Crisp Sets) and Operations on Classical Sets<br/>6.4 Fuzzy Sets and Operations on Fuzzy Sets<br/>6.5 Membership Functions (MFs)<br/>6.6 Points to Remember<br/>Questions<br/>7. FUZZY LOGIC SYSTEM COMPONENTS<br/>7.1 Introduction to Fuzzification<br/>7.2 Membership Value Assignment<br/>7.3 Generation of Rules and Decision Making System<br/>7.4 Inference Methods<br/><br/>7.5 Configuration of a Fuzzy Logic Controller (FLC) 143<br/>7.6 Diefuzzification Methods 152<br/>7.7 Design Procedure of a Fuzzy Logic Controller 154<br/>7.8 Analog Design Approach to a Simple Fuzzy Computer 156<br/>7.9 Points to Remember 160<br/>Questions 161<br/>8. APPLICATIONS OF ANNs AND FUZZY LOGIC<br/>8.1 Introduction 164<br/>8.2 Process Identification arid Control 164<br/>8.3 Fault Diagnosis 167<br/>8.4 Load Forecasting Using an ANN 172<br/>8.5 Applications of ANNs in Renewable Energy Systems 179<br/>8.6 Applicationsof ANNs in Other Energy Systems 179<br/>8.7 Applications of ANNs in Forecasting and Prediction 181<br/>8.8 Fuzzy Logic Control 181<br/>8.9 Applications of Fuzzy Logic Control 183<br/>8.10 Points to Remember 190<br/>Questions 191<br/>9. NON-TRADITIONAL OPTIMIZED ALGORITHMS-GENETIC ALGORITHM<br/>9.1 Introduction to Genetic Algorithms 194<br/>9.2 Basic Terminology of Biology and Genetic Algorithms 195<br/>9.3 Comparison between Genetic Algorithms and Other Traditional Algorithms 196<br/>9.4 Overview of Genetic Algorithms 196<br/>9.5 Lybrinthinism in Optimization 197<br/>9.6 Generalized Steps of a Genetic Algorithm 198<br/>9.7 The Modified Genetic Algorithm 207<br/>9.8 Applications of Genetic Algorithms in Engineering Design 209<br/>9.9 Current and Future Trends of Optimized Evolutionary Algorithms 213<br/>9.10 Points to Remember<br/>Questions ' |
650 ## - SUBJECT | |
Keyword | Neural networks |
700 ## - ADDED ENTRY--PERSONAL NAME | |
Personal name | Rao, T. Nageswara |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | General 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 VAL/I | P40358 | 03/07/2023 | 24/05/2023 | General Books |