Soft Computing : (Record no. 25621)
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| 000 -LEADER | |
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| fixed length control field | 09303nam a22003853i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC5125313 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190107103930.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 181231s2013 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789332514201 |
| Qualifying information | (electronic bk.) |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC5125313 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL5125313 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (CaONFJC)MIL514410 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)883377632 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 23 |
| Classification number | 006.32 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Roy, Samir. |
| 245 10 - TITLE STATEMENT | |
| Title | Soft Computing : |
| Remainder of title | Neuro-Fuzzy and Genetic Algorithms. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (609 pages) |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 -- Chapter Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- Chapter 7: Pattern Classifiers -- 7.1 Hebb Nets -- 7.2 Perceptrons -- 7.3 Adaline -- 7.4 Madaline -- Chapter Summary. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- 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 -- Chapter Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercise -- Bibliography and Historical Notes -- 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 -- Chapter Summary -- Test Your Knowledge -- Answers -- Bibliography and Historical Notes -- Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chakraborty, Udit. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Print version: |
| Main entry heading | Roy, Samir |
| Title | Soft Computing : Neuro-Fuzzy and Genetic Algorithms |
| Place, publisher, and date of publication | Noida : Pearson India,c2013 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/cethalassery/detail.action?docID=5125313">https://ebookcentral.proquest.com/lib/cethalassery/detail.action?docID=5125313</a> |
| Public note | Click to View |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Materials specified (bound volume or other part) | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Online access | CENTRAL LIBRARY | Digital Library | Digital Library | 07/01/2019 | 006.32 ROY-S | E0065 | 07/01/2019 | 07/01/2019 | E- Books |