Principles of Soft Computing / (Record no. 25542)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 10825nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20201130123428.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190212b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788126577132 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | VITAP |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 23rd |
| Classification number | 006.3 SIV |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 10255 |
| Personal name | Sivanandam, S. V. |
| 245 ## - TITLE STATEMENT | |
| Title | Principles of Soft Computing / |
| Statement of responsibility, etc. | S. N. Sivanandam and S. N. Deepa |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 3rd Ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | Wiley India Pvt. Ltd. |
| Date of publication, distribution, etc. | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxvii, 760p. : ill. ; |
| Other physical details | 28cm |
| 500 ## - GENERAL NOTE | |
| General note | It includes bibliography, sample question papers and Index Pages |
| 521 ## - TARGET AUDIENCE NOTE | |
| Target audience note | This book is meant for a wide range of readers, who wish to learn the basic concepts of soft computing. It can also be useful for programmers, researchers and management experts who use soft computing techniques. The basic concepts of soft computing are dealt in detail with the relevant information and knowledge available for understanding the computing process. The various neural network concepts are explained with examples, highlighting the difference between various architectures. Fuzzy logic techniques have been clearly dealt with suitable examples. Genetic algorithm operators and the various classifications have been discussed in lucid manner, so that a starter can understand the concepts with a minimal effort.<br/><br/>Table of Contents:<br/>Chapter 1 Introduction<br/>1.1 Neural Networks<br/>1.2 Application Scope of Neural Networks<br/>1.3 Fuzzy Logic<br/>1.4 Genetic Algorithm<br/>1.5 Hybrid Systems<br/>1.6 Soft Computing<br/>1.7 Summary<br/>Chapter 2 Artificial Neural Network: An Introduction<br/>2.1 Fundamental Concept<br/>2.2 Evolution of Neural Networks<br/>2.3 Basic Models of Artificial Neural Network<br/>2.4 Important Terminologies of ANNs<br/>2.5 McCulloch–Pitts Neuron<br/>2.6 Linear Separability<br/>2.7 Hebb Network<br/>2.8 Summary<br/>2.9 Solved Problems<br/>2.10 Review Questions<br/>2.11 Exercise Problems<br/>2.12 Projects<br/>Chapter 3 Supervised Learning Network<br/>3.1 Introduction<br/>3.2 Perceptron Networks<br/>3.3 Adaptive Linear Neuron (Adaline)<br/>3.4 Multiple Adaptive Linear Neurons<br/>3.5 Back-Propagation Network<br/>3.6 Radial Basis Function Network<br/>3.7 Time Delay Neural Network<br/>3.8 Functional Link Networks<br/>3.9 Tree Neural Networks<br/>3.10 Wavelet Neural Networks<br/>3.11 Summary<br/>3.12 Solved Problems<br/>3.13 Review Questions<br/>3.14 Exercise Problems<br/>3.15 Projects<br/>Chapter 4 Associative Memory Networks<br/>4.1 Introduction<br/>4.2 Training Algorithms for Pattern Association<br/>4.3 Autoassociative Memory Network<br/>4.4 Heteroassociative Memory Network<br/>4.5 Bidirectional Associative Memory (BAM)<br/>4.6 Hopfield Networks<br/>4.7 Iterative Autoassociative Memory Networks<br/>4.8 Temporal Associative Memory Network<br/>4.9 Summary<br/>4.10 Solved Problems<br/>4.11 Review Questions<br/>4.12 Exercise Problems<br/>4.13 Projects<br/>Chapter 5 Unsupervised Learning Networks<br/>5.1 Introduction<br/>5.2 Fixed Weight Competitive Nets<br/>5.3 Kohonen Self-Organizing Feature Maps<br/>5.4 Learning Vector Quantization<br/>5.5 Counterpropagation Networks<br/>5.6 Adaptive Resonance Theory Network<br/>5.7 Summary<br/>5.8 Solved Problems<br/>5.9 Review Questions<br/>5.10 Exercise Problems<br/>5.11 Projects<br/>Chapter 6 Special Networks<br/>6.1 Introduction<br/>6.2 Simulated Annealing Network<br/>6.3 Boltzmann Machine<br/>6.4 Gaussian Machine<br/>6.5 Cauchy Machine<br/>6.6 Probabilistic Neural Net<br/>6.7 Cascade Correlation Network<br/>6.8 Cognitron Network<br/>6.9 Neocognitron Network<br/>6.10 Cellular Neural Network<br/>6.11 Logicon Projection Network Model<br/>6.12 Spatio-Temporal Connectionist Neural Network<br/>6.13 Optical Neural Networks<br/>6.14 Neuroprocessor Chips<br/>6.15 Ensemble Neural Network Models<br/>6.16 Summary<br/>6.17 Review Questions<br/>Chapter 7 Third-Generation Neural Networks<br/>7.1 Introduction<br/>7.2 Spiking Neural Networks<br/>7.3 Convolutional Neural Networks<br/>7.4 Deep Learning Neural Networks<br/>7.5 Extreme Learning Machine Model<br/>7.6 Summary<br/>7.7 Review Questions<br/>Chapter 8 Clustering of Self-Organizing Feature Maps<br/>8.1 Introduction<br/>8.2 Concept of Clustering<br/>8.3 Training of SOMs<br/>8.4 Clustering of SOM: Method I<br/>8.5 Clustering of SOM: Method II<br/>8.5 Summary<br/>8.6 Review Questions<br/>Chapter 9 Stability Analysis of a Class of Artificial Neural Network Systems<br/>9.1 Introduction<br/>9.2 Stability Conditions of a Class of Non-Linear Systems<br/>9.3 Formation of Main Matrices and Sub-Matrices for an Artificial Neural Network System<br/>9.4 Methodology Developed for Stability Analysis of Artificial Neural Networks<br/>9.5 Summary<br/>9.6 Solved Problems<br/>9.7 Review Questions<br/>9.8 Exercise Problems<br/>Chapter 10 Introduction to Fuzzy Logic, Classical Sets and Fuzzy Sets<br/>10.1 Introduction to Fuzzy Logic<br/>10.2 Classical Sets (Crisp Sets)<br/>10.3 Fuzzy Sets<br/>10.4 Summary<br/>10.5 Solved Problems<br/>10.6 Review Questions<br/>10.7 Exercise Problems<br/>Chapter 11 Classical Relations and Fuzzy Relations<br/>11.1 Introduction<br/>11.2 Cartesian Product of Relation<br/>11.3 Classical Relation<br/>11.4 Fuzzy Relations<br/>11.5 Tolerance and Equivalence Relations<br/>11.6 Noninteractive Fuzzy Sets<br/>11.7 Summary<br/>11.8 Solved Problems<br/>11.9 Review Questions<br/>11.10 Exercise Problems<br/>Chapter 12 Membership Function<br/>12.1 Introduction<br/>12.2 Features of the Membership Functions<br/>12.3 Fuzzification<br/>12.4 Methods of Membership Value Assignments<br/>12.5 Summary<br/>12.6 Solved Problems<br/>12.7 Review Questions<br/>12.8 Exercise Problems<br/>Chapter 13 Defuzzification<br/>13.1 Introduction<br/>13.2 Lambda-Cuts for Fuzzy Sets (Alpha-Cuts)<br/>13.3 Lambda-Cuts for Fuzzy Relations<br/>13.4 Defuzzification Methods<br/>13.5 Summary<br/>13.6 Solved Problems<br/>13.7 Review Questions<br/>13.8 Exercise Problems<br/>Chapter 14 Fuzzy Arithmetic and Fuzzy Measures<br/>14.1 Introduction<br/>14.2 Fuzzy Arithmetic<br/>14.3 Extension Principle<br/>14.4 Fuzzy Measures<br/>14.5 Measures of Fuzziness<br/>14.6 Fuzzy Integrals<br/>14.7 Summary<br/>14.8 Solved Problems<br/>14.9 Review Questions<br/>14.10 Exercise Problems<br/>Chapter 15 Fuzzy Rule Base and Approximate Reasoning<br/>15.1 Introduction<br/>15.2 Truth Values and Tables in Fuzzy Logic<br/>15.3 Fuzzy Propositions<br/>15.4 Formation of Rules<br/>15.5 Decomposition of Rules (Compound Rules)<br/>15.6 Aggregation of Fuzzy Rules<br/>15.7 Fuzzy Reasoning (Approximate Reasoning)<br/>15.8 Fuzzy Inference Systems (FIS)<br/>15.9 Overview of Fuzzy Expert System<br/>15.10 Summary<br/>15.11 Review Questions<br/>15.12 Exercise Problems<br/>Chapter 16 Fuzzy Decision Making<br/>16.1 Introduction<br/>16.2 Individual Decision Making<br/>16.3 Multiperson Decision Making<br/>16.4 Multiobjective Decision Making<br/>16.5 Multiattribute Decision Making<br/>16.6 Fuzzy Bayesian Decision Making<br/>16.7 Summary<br/>16.8 Review Questions<br/>16.9 Exercise Problems<br/>Chapter 17 Fuzzy Logic Control Systems<br/>17.1 Introduction<br/>17.2 Control System Design<br/>17.3 Architecture and Operation of FLC System<br/>17.4 FLC System Models<br/>17.5 Application of FLC Systems<br/>17.6 Summary<br/>17.7 Review Questions<br/>17.8 Exercise Problems<br/>Chapter 18 Fuzzy Cognitive Maps<br/>18.1 Cognitive Maps – Base for FCM<br/>18.2 Fundamentals of FCM<br/>18.3 Dynamics of FCM and Its Activation Function<br/>18.4 Applications of FCM<br/>18.5 Summary<br/>18.6 Review Questions<br/>Chapter 19 Type-2 Fuzzy Sets and Embedded Fuzzy Sets<br/>19.1 Basic Concepts and Definition of Type-2 Fuzzy Sets<br/>19.2 Set Theoretic and Algebraic Operations on Type-2 Fuzzy Sets<br/>19.3 Properties of Membership Grades<br/>19.4 Cartesian Product of Type-2 Fuzzy Sets<br/>19.5 Composition of Type-2 Fuzzy Sets<br/>19.6 Interval Type-2 Fuzzy Sets<br/>19.7 Applications of Type-2 Fuzzy Sets<br/>19.8 Embedded Fuzzy Sets<br/>19.9 Summary<br/>19.10 Review Questions<br/>Chapter 20 Stability Analysis of Certain Classes of Fuzzy Systems<br/>20.1 Stability Analysis of Fuzzy Systems given by System Matrices<br/>20.2 Numerical Illustrations for Fuzzy System Stability<br/>20.3 Stability Analysis of Fuzzy Systems represented by Relational Matrices<br/>20.4 Stabilization and Stability Analysis of an Inverted Pendulum Motion using Fuzzy Logic Controller<br/>20.5 Summary<br/>20.6 Review Questions<br/>20.7 Exercise Problems<br/>Chapter 21 Genetic Algorithm<br/>21.1 Introduction<br/>21.2 Biological Background<br/>21.3 Traditional Optimization and Search Techniques<br/>21.4 Genetic Algorithm and Search Space<br/>21.5 Genetic Algorithm vs. Traditional Algorithms<br/>21.6 Basic Terminologies in Genetic Algorithm<br/>21.7 Simple GA<br/>21.8 General Genetic Algorithm<br/>21.9 Operators in Genetic Algorithm<br/>21.10 Stopping Condition for Genetic Algorithm Flow<br/>21.11 Constraints in Genetic Algorithm<br/>21.12 Problem Solving Using Genetic Algorithm<br/>21.13 The Schema Theorem<br/>21.14 Classification of Genetic Algorithm<br/>21.15 Holland Classifier Systems<br/>21.16 Genetic Programming<br/>21.17 Advantages and Limitations of Genetic Algorithm<br/>21.18 Applications of Genetic Algorithm<br/>21.19 Summary<br/>21.20 Review Questions<br/>21.21 Exercise Problems<br/>Chapter 22 Differential Evolution Algorithm<br/>22.1 Differential Evolution – Process Flow and Operators<br/>22.2 Selection of DE Control Parameters<br/>22.3 Schemes of Differential Evolution<br/>22.4 Numerical Illustration of DE Algorithm for a Simple Function Optimization<br/>22.5 Applications of Differential Evolution<br/>22.6 Summary<br/>22.7 Review Questions<br/>Chapter 23 Hybrid Soft Computing Techniques<br/>23.1 Introduction<br/>23.2 Neuro-Fuzzy Hybrid Systems<br/>23.3 Genetic Neuro-Hybrid Systems<br/>23.4 Genetic Fuzzy Hybrid and Fuzzy Genetic Hybrid Systems<br/>23.5 Simplified Fuzzy ARTMAP<br/>23.6 Summary<br/>23.7 Solved Problems using MATLAB<br/>23.8 Review Questions<br/>23.9 Exercise Problems xxiv<br/>Chapter 24 Applications of Soft Computing<br/>24.1 Introduction<br/>24.2 A Fusion Approach of Multispectral Images with SAR (Synthetic Aperture Radar) Image for Flood Area<br/>24.3 Optimization of Traveling Salesman Problem using Genetic Algorithm Approach<br/>24.4 Genetic Algorithm-Based Internet Search Technique<br/>24.5 Soft Computing Based Hybrid Fuzzy Controllers<br/>24.6 Soft Computing Based Rocket Engine Control<br/>24.7 Summary<br/>24.8 Review Questions<br/>24.9 Exercise Problems<br/>Chapter 25 Soft Computing Techniques Using C and C++<br/>25.1 Introduction<br/>25.2 Neural Network Implementation<br/>25.3 Fuzzy Logic Implementation<br/>25.4 Genetic Algorithm Implementation<br/>25.5 Summary<br/>25.6 Exercise Problems<br/>Chapter 26 MATLAB Environment for Soft Computing Technique<br/>26.1 Introduction<br/>26.2 Getting Started with MATLAB<br/>26.3 Introduction to Simulink<br/>26.4 MATLAB Neural Network Toolbox<br/>26.5 Fuzzy Logic MATLAB Toolbox<br/>26.6 Genetic Algorithm MATLAB Toolbox<br/>26.7 Neural Network MATLAB Source Codes<br/>26.8 Fuzzy Logic MATLAB Source Codes<br/>26.9 Genetic Algorithm MATLAB Source Codes<br/>26.10 Summary<br/>26.11 Exercise Problems<br/><br/>Bibliography<br/>Sample Question Paper 1<br/>Sample Question Paper 2<br/>Sample Question Paper 3<br/>Sample Question Paper 4<br/>Sample Question Paper 5<br/>Index xx |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 10256 |
| Topical term or geographic name entry element | Neural networks (Computer science); Soft computing; Artificial intelligence; Operations research; Computer science |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 10257 |
| Personal name | Deepa, S.N. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://www.wileyindia.com/principles-of-soft-computing-3ed.html">https://www.wileyindia.com/principles-of-soft-computing-3ed.html</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Text Book |
| Edition | 23rd Ed. |
| Classification part | 006.3 SIV |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Materials specified (bound volume or other part) | Damaged status | Use restrictions | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type | Public note | Total Renewals | Date last checked out | Checked out |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Paper Back | Restricted Access | Not For Loan | Reference | School of Computer Science Section | VIT-AP | General Stacks | 2019-02-02 | Bookionics | 749.00 | VJ44186 | 006.3 SIV | 015261 | 2019-02-12 | 2019-02-12 | Reference Book | CSE | |||||||
| Dewey Decimal Classification | Paper Back | School of Computer Science Section | VIT-AP | General Stacks | 2019-02-02 | Bookionics | 749.00 | VJ44186 | 27 | 006.3 SIV | 015262 | 2025-04-29 | 2019-02-12 | Text Book | CSE | 3 | 2025-04-23 | |||||||
| Dewey Decimal Classification | Paper Back | School of Computer Science Section | VIT-AP | General Stacks | 2019-02-02 | Bookionics | 749.00 | VJ44186 | 20 | 006.3 SIV | 015263 | 2025-05-07 | 2019-02-12 | Text Book | CSE | 6 | 2025-04-26 | |||||||
| Dewey Decimal Classification | Paper Back | School of Computer Science Section | VIT-AP | General Stacks | 2019-02-02 | Bookionics | 749.00 | VJ44186 | 24 | 006.3 SIV | 015264 | 2025-04-29 | 2019-02-12 | Text Book | CSE | 7 | 2025-04-23 | |||||||
| Dewey Decimal Classification | Paper Back | School of Computer Science Section | VIT-AP | General Stacks | 2019-02-02 | Bookionics | 749.00 | VJ44186 | 20 | 006.3 SIV | 015265 | 2025-04-17 | 2019-02-12 | Text Book | CSE | 5 | 2025-04-17 | 2025-10-14 |