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Genetic Algorithms for Control and Signal Processing

Author: Kim F Man; Kit S Tang; Sam Kwong; Wolfgang A Halang
Publisher: London : Springer London, 1997.
Series: Advances in industrial control.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This volume provides comprehensive coverage of the emerging field of Genetic Algorithms and their application in solving engineering problems in control and signal processing. The basic genetic operations such as crossover, mutation and reinsertion are discussed, and the characteristics of genetic algorithms, their advantages and constraints, are also described. These processes are illustrated by real-world  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Kim F Man; Kit S Tang; Sam Kwong; Wolfgang A Halang
ISBN: 9781447109556 1447109554
OCLC Number: 853258737
Description: 1 online resource (xx, 211 pages 127 illustrations).
Contents: 1. Introduction, Background and Biological Inspiration.- 1.1 Biological Background.- 1.1.1 Coding of DNA.- 1.1.2 Flow of Genetic Information.- 1.1.3 Recombination.- 1.1.4 Mutation.- 1.2 Conventional Genetic Algorithm.- 1.3 Theory and Hypothesis.- 1.3.1 Schema Theory.- 1.3.2 Building Block Hypothesis.- 1.4 A Simple Example.- 2. Modification in Genetic Algorithm.- 2.1 Chromosome Representation.- 2.2 Objective and Fitness Functions.- 2.2.1 Linear Scaling.- 2.2.2 Sigma Truncation.- 2.2.3 Power Law Scaling.- 2.2.4 Ranking.- 2.3 Selection Methods.- 2.4 Genetic Operations.- 2.4.1 Crossover.- 2.4.2 Mutation.- 2.4.3 Operational Rates Settings.- 2.4.4 Reordering.- 2.5 Replacement Scheme.- 3. Intrinsic Characteristics.- 3.1 Parallel Genetic Algorithm.- 3.1.1 Global GA.- 3.1.2 Migration GA.- 3.1.3 Diffusion GA.- 3.2 Multiple Objective.- 3.3 Robustness.- 3.4 Multimodal.- 3.5 Constraints.- 3.5.1 Searching Domain.- 3.5.2 Repair Mechanism.- 3.5.3 Penalty Scheme.- 3.5.4 Specialized Genetic Operations.- 4. Advanced GA Applications.- 4.1 Case Study 1: GA in Time Delay Estimation.- 4.1.1 Problem Formulation.- 4.1.2 Genetic Approach.- 4.1.3 Results.- 4.2 Case Study 2: GA in Active Noise Control.- 4.2.1 Problem Formulation.- 4.2.2 Simple Genetic Algorithm.- 4.2.3 Multiobjective Genetic Algorithm Approach.- 4.2.4 Parallel Genetic Algorithm Approach.- 4.2.5 Hardware GA Processor.- 4.3 Case Study 3: GA in Automatic Speech Recognition.- 4.3.1 Warping Path.- 4.3.2 Implementation of Genetic Time Warping.- 4.3.3 Performance Evaluation.- 5. Hierarchical Genetic Algorithm.- 5.1 Biological Inspiration.- 5.1.1 Regulatory Sequences and Structural Genes.- 5.1.2 Active and Inactive Genes.- 5.2 Hierarchical Chromosome Formulation.- 5.3 Genetic Operations.- 5.4 Multiple Objective Approach.- 5.4.1 Iterative Approach.- 5.4.2 Group Technique.- 5.4.3 Multiple-Objective Ranking.- 6. Filtering Optimization.- 6.1 Digital IIR Filter Design.- 6.1.1 Chromosome Coding.- 6.1.2 The Lowest Filter Order Criterion.- 6.2 H-infinity Controller Design.- 6.2.1 A Mixed Optimization Design Approach.- 6.2.2 Hierarchical Genetic Algorithm.- 6.2.3 The Distillation Column Design.- 6.2.4 Design Comments.- 7. Emerging Technology.- 7.1 Neural Networks.- 7.1.1 Introduction of Neural Network.- 7.1.2 HGA Trained Neural Network (HGANN).- 7.1.3 Simulation Results.- 7.1.4 Application of HGANN on Classification.- 7.2 Fuzzy Logic.- 7.2.1 Basic Formulation of Fuzzy Logic Controller.- 7.2.2 Hierarchical Structure.- 7.2.3 Experimental Results.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.
Series Title: Advances in industrial control.
Responsibility: by Kim F. Man, Kit S. Tang, Sam Kwong, Wolfgang A. Halang.

Abstract:

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. New theory, new controllers, actuators, sensors, new industrial processes, computer  Read more...

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