Intelligent Control Systems With Labview

by Ponce-cruz, Pedro; Ramirez-figueroa, Fernando D.

• ISBN13: 9781848826830
• ISBN10: 1848826834
• eBook ISBN(s): 9781848826847
• Edition: DVD
• Format: Hardcover
• Copyright: 2009-12-01
• Publisher: Springer Verlag
• More Book Details
• Purchase Benefits

Summary

Intelligent control is a rapidly developing, complex, and challenging field of increasing practical importance and still greater potential. Its applications have a solid core in robotics and mechatronics but branch out into areas as diverse as process control, automotive industry, medical equipment, renewable energy and air conditioning.Intelligent Control with LabVIEW'„¢ is a fresh and pragmatic approach to the understanding of a subject often clouded by too much mathematical theory. It exploits the full suite of tools provided by LabVIEW'„¢, showing the student how to design, develop, analyze, and visualize intelligent control algorithms quickly and simply. Block diagrams are used to follow the progress of an algorithm through the design process and allow seamless integration with hardware systems for rapid deployment in laboratory experiments.This text delivers a thorough grounding in the main tools of intelligent control:'¢ fuzzy logic systems, emulating human reasoning processes;'¢ artificial neural networks, mimicking our ability to learn;'¢ neuro-fuzzy systems for implementing controller adaptation;'¢ evolutionary methods, using the ideas of genetic evolution for optimization; and'¢ predictive methods, giving information about a system's future behavior.Intelligent Control with LabVIEW'„¢ facilitates the learning and teaching of its subject by:'¢ extensive use of worked examples;'¢ end-of-chapter problems with separate solutions; and'¢ provision of intelligent control tools for LabVIEW'„¢.The complete solutions and LabVIEW'„¢ programs are included on the enclosed DVD.Graduate and senior undergraduate students learning about intelligent control will find great help in this textbook's easy-to-read style and graphical programming approach. The book will be of material assistance to instructors interested in using LabVIEW'„¢-based teaching when designing a course in intelligent control. Researchers in intelligent systems who employ LabVIEW'„¢ as a laboratory tool will also benefit from its dedicated support for intelligent control.

Table of Contents

Intelligent Control for LabVIEW	p. 1
Introduction	p. 1
Intelligent Control in Industrial Applications	p. 3
LabVIEW	p. 4
References	p. 7
Fuzzy Logic	p. 9
Introduction	p. 9
Industrial Applications	p. 9
Background	p. 10
Uncertainty in Information	p. 11
Concept of Fuzziness	p. 11
Foundations of Fuzzy Set Theory	p. 11
Fuzzy Sets	p. 12
Boolean Operations and Terms	p. 14
Fuzzy Operations and Terms	p. 15
Properties of Fuzzy Sets	p. 18
Fuzzification	p. 18
Extension Principle	p. 21
Alpha Cuts	p. 23
The Resolution Principle	p. 24
Fuzziness of Uncertainty	p. 24
Possibility and Probability Theories	p. 25
Fuzzy Logic Theory	p. 26
From Classical to Fuzzy Logic	p. 26
Fuzzy Logic and Approximate Reasoning	p. 26
Fuzzy Relations	p. 28
Properties of Relations	p. 28
Max-Min Composition	p. 29
Max-Star Composition	p. 30
Max-Average Composition	p. 31
Fuzzy Linguistic Descriptions	p. 31
The Fuzzy Logic Controller	p. 33
Linguistic Variables	p. 33
Membership Functions	p. 33
Rules Evaluation	p. 33
Mamdani Fuzzy Controller	p. 34
Structure	p. 34
Fuzzification	p. 34
Rules Evaluation	p. 35
Defuzzification	p. 35
Tsukamoto Fuzzy Controller	p. 35
Takagi-Sugeno Fuzzy Controller	p. 36
Structure	p. 36
Fuzzification	p. 36
Rules Evaluation	p. 36
Crisp Outputs	p. 37
Implementation of the Fuzzy Logic Controllers Using the Intelligent Control Toolkit for LabVIEW	p. 37
Fuzzification	p. 38
Rules Evaluation	p. 40
Defuzzification: Crisp Outputs	p. 41
Classical Control Example	p. 43
References	p. 46
Futher Reading	p. 46
Artificial Neural Networks	p. 47
Introduction	p. 47
Artificial Neural Network Classification	p. 55
Artificial Neural Networks	p. 56
Perception	p. 57
Multi-layer Neural Network	p. 60
Trigonometric Neural Networks	p. 71
Kohonen Maps	p. 79
Bayesian or Belief Networks	p. 84
References	p. 87
Futher Reading	p. 88
Neuro-fuzzy Controller Theory and Application	p. 89
Introduction	p. 89
The Neuro-fuzzy Controller	p. 90
Trigonometric Artificial Neural Networks	p. 91
Fuzzy Cluster Means	p. 96
Predictive Method	p. 98
Results Using the Controller	p. 100
Controller Enhancements	p. 101
ANFIS: Adaptive Neuro-fuzzy Inference Systems	p. 106
ANFIS Topology	p. 108
References	p. 122
Futher Reading	p. 122
Genetic Algorithms and Genetic Programming	p. 123
Introduction	p. 123
Evolutionary Computation	p. 123
Industrial Applications	p. 124
Biological Terminology	p. 125
Search Spaces and Fitness	p. 125
Encoding and Decoding	p. 125
Genetic Algorithm Stages	p. 126
Initialization	p. 127
Selection	p. 128
Crossover	p. 129
Mutation	p. 130
Genetic Algorithms and Traditional Search Methods	p. 134
Applications of Genetic Algorithms	p. 135
Pros and Cons of Genetic Algorithms	p. 136
Selecting Genetic Algorithm Methods	p. 136
Messy Genetic Algorithm	p. 137
Optimization of Fuzzy Systems Using Genetic Algorithms	p. 138
Coding Whole Fuzzy Partitions	p. 138
Standard Fitness Functions	p. 139
Coding Rule Bases	p. 139
An Application of the ICTL for the Optimization of a Navigation System for Mobile Robots	p. 140
Genetic Programming Background	p. 143
Genetic Programming Definition	p. 143
Historical Background	p. 144
Industrial Applications	p. 144
Advantages of Evolutionary Algorithms	p. 144
Genetic Programming Algorithm	p. 145
Length	p. 146
Genetic Programming Stages	p. 146
Initialization	p. 146
Fitness	p. 147
Selection	p. 147
Crossover	p. 147
Mutation	p. 148
Variations of Genetic Programming	p. 149
Genetic Programming in Data Modeling	p. 150
Genetic Programming Using the ICTL	p. 150
References	p. 153
Futher Reading	p. 154
Simulated Annealing, FCM, Partition Coefficients and Tabu Search	p. 155
Introduction	p. 155
Introduction to Simulated Annealing	p. 156
Pattern Recognition	p. 157
Introduction to Tabu Search	p. 157
Industrial Applications of Simulated Annealing	p. 158
Industrial Applications of Fuzzy Clustering	p. 158
Industrial Applications of Tabu Search	p. 158
Simulated Annealing	p. 159
Simulated Annealing Algorithm	p. 161
Sample Iteration Example	p. 163
Example of Simulated Annealing Using the Intelligent Control Toolkit for LabVIEW	p. 163
Fuzzy Clustering Means	p. 166
FCM Example	p. 170
Partition Coefficients	p. 172
Reactive Tabu Search	p. 173
Introduction to Reactive Tabu Search	p. 173
Memory	p. 174
References	p. 189
Futher Reading	p. 190
Predictors	p. 191
Introduction to Forecasting	p. 191
Industrial Applications	p. 192
Forecasting Methods	p. 193
Qualitative Methods	p. 193
Quantitative Methods	p. 194
Regression Analysis	p. 194
Exponential Smoothing	p. 194
Simple-exponential Smoothing	p. 195
Simple-exponential Smoothing Algorithm	p. 195
Double-exponential Smoothing	p. 196
Holt-Winter Method	p. 197
Non-seasonal Box-Jenkins Models	p. 198
General Box-Jenkins Model	p. 199
Minimum Variance Estimation and Control	p. 200
Example of Predictors Using the Intelligent Control Toolkit for LabVIEW (ICTL)	p. 202
Exponential Smoothing	p. 202
Box-Jenkins Method	p. 203
Minimum Variance	p. 204
Gray Modeling and Prediction	p. 205
Modeling Procedure of the Gray System	p. 206
Example of a Gray Predictor Using the ICTL	p. 207
References	p. 210
Futher Reading	p. 210
Index	p. 211
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