Soft Computing and Fractal Theory for Intelligent Manufacturing / Edition 1

Soft Computing and Fractal Theory for Intelligent Manufacturing / Edition 1

by Oscar Castillo, Patricia Melin
     
 

ISBN-10: 3790815470

ISBN-13: 9783790815474

Pub. Date: 03/10/2003

Publisher: Physica-Verlag HD

The book describes the application of soft computing techniques and fractal theory to intelligent manufacturing. Hybrid intelligent systems, which integrate different soft computing techniques and fractal theory, are also presented. The text covers the basics of fuzzy logic, neural networks, genetic algorithms, simulated annealing, chaos and fractal theory. It also

Overview

The book describes the application of soft computing techniques and fractal theory to intelligent manufacturing. Hybrid intelligent systems, which integrate different soft computing techniques and fractal theory, are also presented. The text covers the basics of fuzzy logic, neural networks, genetic algorithms, simulated annealing, chaos and fractal theory. It also describes in detail different hybrid architectures for developing intelligent manufacturing systems for applications in automated quality control, process monitoring and diagnostics, adaptive control of non-linear plants, and time series prediction. Real-world applications covered in this book include: tuning of televisions, battery charging, sound speaker testing, fruit classification, and stepping motors.

Product Details

ISBN-13:
9783790815474
Publisher:
Physica-Verlag HD
Publication date:
03/10/2003
Series:
Studies in Fuzziness and Soft Computing Series, #117
Edition description:
2003
Pages:
283
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1 Introduction.- 2 Type-1 Fuzzy Logic.- 2.1 Type-1 Fuzzy Set Theory.- 2.2 Fuzzy Rules and Fuzzy Reasoning.- 2.2.1 Fuzzy Relations.- 2.2.2 Fuzzy Rules.- 2.3 Fuzzy Inference Systems.- 2.4 Fuzzy Modelling.- 2.5 Summary.- 3 Type-2 Fuzzy Logic.- 3.1 Type-2 Fuzzy Sets.- 3.2 Operations of Type-2 Fuzzy Sets.- 3.3 Type-2 Fuzzy Systems.- 3.3.1 Singleton Type-2 Fuzzy Logic Systems.- 3.3.2 Non-Singleton Fuzzy Logic Systems.- 3.3.3 Sugeno Type-2 Fuzzy Systems.- 3.4 Summary.- 4 Supervised Learning Neural Networks.- 4.1 Backpropagation for Feedforward Networks.- 4.1.1 The Backpropagation Learning Algorithm.- 4.1.2 Backpropagation Multilayer Perceptrons.- 4.1.3 Methods for Speeding up Backpropagation.- 4.2 Radial Basis Function Networks.- 4.3 Adaptive Neuro-Fuzzy Inference Systems.- 4.3.1 ANFIS Architecture.- 4.3.2 Learning Algorithm.- 4.4 Summary.- 5 Unsupervised Learning Neural Networks.- 5.1 Competitive Learning Networks.- 5.2 Kohonen Self-Organizing Networks.- 5.3 Learning Vector Quantization.- 5.4 The Hopfield Network.- 5.5 Summary.- 6 Genetic Algorithms and Simulated Annealing.- 6.1 Genetic Algorithms.- 6.2 Modifications to Genetic Algorithms.- 6.2.1 Chromosome Representation.- 6.2.2 Objective Function and Fitness.- 6.2.3 Selection Methods.- 6.2.4 Genetic Operations.- 6.2.5 Parallel Genetic Algorithm.- 6.3 Simulated Annealing.- 6.4 Applications of Genetic Algorithms.- 6.4.1 Evolving Neural Networks.- 6.4.1.1 Evolving Weights in a Fixed Network.- 6.4.1.2 Evolving Network Architectures.- 6.4.2 Evolving Fuzzy Systems.- 6.5 Summary.- 7 Dynamical Systems Theory.- 7.1 Basic Concepts of Dynamical Systems.- 7.2 Controlling Chaos.- 7.2.1 Controlling Chaos through Feedback.- 7.2.1.1 Ott-Grebogi-Yorke Method.- 7.2.1.2 Pyragas’s Control Methods.- 7.2.1.3 Controlling Chaos by Chaos.- 7.2.2 Controlling Chaos without Feedback.- 7.2.2.1 Control through Operating Conditions.- 7.2.2.2 Control by System Design.- 7.2.2.3 Taming Chaos.- 7.2.3 Method Selection.- 7.3 Summary.- 8 Plant Monitoring and Diagnostics.- 8.1 Monitoring and Diagnosis.- 8.2 Fractal Dimension of a Geometrical Object.- 8.3 Fuzzy Estimation of the Fractal Dimension.- 8.4 Plant Monitoring with Fuzzy-Fractal Approach.- 8.5 Experimental Results.- 8.6 Summary.- 9 Adaptive Control of Non-Linear Plants.- 9.1 Fundamental Adaptive Fuzzy Control Concept.- 9.2 Basic Concepts of Stepping Motors.- 9.2.1 Variable Reluctance Motors.- 9.2.2 Unipolar Motors.- 9.2.3 Bipolar Motors.- 9.2.4 Dynamics of the Stepping Motor.- 9.2.5 Control of the Stepping Motor.- 9.3 Fuzzy Logic Controller of the Stepping Motor.- 9.4 Hardware Implementation of ANFIS.- 9.5 Experimental Results.- 9.6 Summary.- 10 Automated Quality Control in Sound Speaker Manufacturing.- 10.1 Introduction.- 10.2 Basic Concepts of Sound Speakers.- 10.2.1 Sound Basics.- 10.2.2 Making Sound.- 10.2.3 Chunks of the Frequency Range.- 10.2.4 Boxes of Sound.- 10.2.5 Alternative Speaker Designs.- 10.3 Description of the Problem.- 10.4 Fractal Dimension of a Sound Signal.- 10.5 Experimental Results.- 10.6 Summary.- 11 Intelligent Manufacturing of Television Sets.- 11.1 Introduction.- 11.2 Imaging System of the Television Set.- 11.2.1 The Cathode Ray Tube.- 11.2.2 Phosphor.- 11.2.3 The Black-and-White TV Signal.- 11.2.4 Adding Color.- 11.3 Breeder Genetic Algorithm for Optimization.- 11.3.1 Genetic Algorithm for Optimization.- 11.4 Automated Electrical Tuning of Television Sets.- 11.5 Intelligent System for Control.- 11.6 Simulation Results.- 11.7 Summary.- 12 Intelligent Manufacturing of Batteries.- 12.1 Intelligent Control of the Battery Charging Process.- 12.1.1 Problem Description.- 12.1.2 Fuzzy Method for Control.- 12.1.3 Neuro-Fuzzy Method for Control.- 12.1.4 Neuro-Fuzzy-Genetic Method for Control.- 12.2 Hardware Implementation of the Fuzzy Controller for the Charging Process.- 12.2.1 Introduction.- 12.2.2 Fuzzy Control.- 12.2.3 Implementation of the Fuzzy Controller.- 12.2.4 Experimental Results.- 12.3 Automated Quality Control of Batteries.- 12.3.1 Introduction.- 12.3.2 Fuzzy Controller.- 12.3.3 Fuzzy Control Implementation.- 12.4 Summary.

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