Industrial Intelligent Control Fundamentals and Applications,9780471950585

Industrial Intelligent Control Fundamentals and Applications

by
Edition: 1st
ISBN13: 9780471950585
ISBN10: 0471950580
Format: Hardcover
Pub. Date: 1996-05-01
Publisher(s): WILEY
List Price: $441.32

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Summary

With a strong emphasis on applications of intelligent control, this extremely accessible book covers the fundamentals, methodologies, architectures and algorithms of automatic control systems. The author summarizes several current concepts to improve industrial control systems, combining classical control techniques of dynamic modeling and control with new approaches discussed in the text. Addresses such intelligent systems as neural networks, fuzzy logic, ruled based, and genetic algorithms. Demonstrates how to develop, design and use intelligent systems to solve sophisticated industrial control problems. Includes numerous worked application examples.

Author Biography

Yong-Zai Lu is the author of Industrial Intelligent Control: Fundamentals and Applications, published by Wiley.

Table of Contents

Foreword xiii(2)
Preface xv(4)
Acknowledgements xix
1 Introduction
1(34)
1.1 Introduction to industrial control
1(4)
1.1.1 Historical review
1(1)
1.1.2 General architecture of industrial control
2(3)
1.2 Highlights of modern control
5(14)
1.2.1 System state space representation
5(4)
1.2.2 System structure properties
9(3)
1.2.3 System indentification and estimation
12(2)
1.2.4 Optimal and optimization control
14(2)
1.2.5 Robust and adaptive control
16(1)
1.2.6 Large scale system methodology
17(1)
1.2.7 Concluding remarks
18(1)
1.3 System Environment
19(5)
1.3.1 Concept of system environment
19(1)
1.3.2 Categories of system environment
20(1)
1.3.2 System Uncertainity
21(3)
1.4 Information and knowledge
24(3)
1.4.1 System information
24(1)
1.4.2 System knowledge
25(2)
1.5 Introduction and knowledge
27(6)
1.5.1 What are intelligent systems?
27(1)
1.5.2 Major characteristics of intelligent systems
28(3)
1.5.3 Intelligent control strategies
31(2)
1.6 Overall structure of this book
33(2)
2 Fundamental techniques for intelligent control
35(46)
2.1 Fuzzy logic and fuzzy systems
35(8)
2.1.1 Introduction
35(1)
2.1.2 Fuzzy sets and fuzzy membership
36(3)
2.1.3 Fuzzy knowledge model
39(2)
2.1.4 Fuzziness and probablity
41(2)
2.2 Fuzzy modelling and fuzzy associative memory
43(6)
2.2.1 Fuzzy relational matrix
44(1)
2.2.2 Composition of fuzzy relation
45(1)
2.2.3 Development of fuzzy associative memories
46(3)
2.2.4 FAM inference
49(1)
2.3 Introduction to neural networks
49(6)
2.3.1 Major functions of neural networks
49(1)
2.3.2 Neurons and neural networks
50(1)
2.3.3 Signal activation functions
51(4)
2.4 Neural networks as function approximation
55(4)
2.5 Hopfield neural networks and Boltzmann machine
59(5)
2.5.1 Hopfield Associative neural networks
60(1)
2.5.2 Hopfield optimization neural networks
61(2)
2.5.3 Boltzmann machine
63(1)
2.6 Self-organizing networks
64(2)
2.7 Dual (mean-variance) connections
66(1)
2.8 Signal coding methods
67(2)
2.8.1 Min-max linear approach
67(1)
2.8.2 Mean-variance approach
68(1)
2.9 Structured knowledge representation
69(12)
2.9.1 Introduction
69(2)
2.9.2 Applications of fuzzy knowledge in expert system
71(3)
2.9.3 Rule-based infernce by neural network
74(2)
2.9.4 Fuzzy rule-based neural network
76(5)
3 Learning strategiesand algorithms
81(6)
3.1 Position of learning in industrial control
81(7)
3.1.1 Learning in system modeling
82(1)
3.1.2 Learning in state estimation
83(3)
3.1.3 Learning in adaptive control
86(1)
3.1.4 Learning in optimization control and decision
87(1)
3.1.5 Learning for self organization and knowledge acquisition
87(1)
3.2 Introduction to learning mechanism
88(4)
3.2.1 Introduction
88(1)
3.2.2 Learning, generalisation and adaption
89(1)
3.2.3 Classification of learning approaches
89(3)
3.3 Supervised learning I: Neural network BP Learning
92(6)
3.3.1 Problem formulation
92(1)
3.3.2 The generalized delta rule
92(1)
3.3.3 Pattern-wise BP learning
93(3)
3.3.4 Batch-wise conjugate gradient BP learning
96(2)
3.4 Generalization and robustness
98(13)
3.4.1 Problem statement
98(1)
3.4.2 Neural network topology and generalization
98(3)
3.4.3 Selection of the training samples
101(5)
3.4.4 Quality of training patterns
106(2)
3.4.5 Learning pace and generalization
108(2)
3.4.6 Concluding remarks
110(1)
3.5 Reinforcement learning
111(5)
3.5.1 Introduction
111(1)
3.5.2 Reinforcement learning algorithms
111(5)
3.6 Self-organized learning
116(3)
3.6.1 Introduction
116(1)
3.6.2 Kohonen's self-organizing feature maps
116(3)
3.7 Adaline adaptive learning
119(3)
3.7.1 Introduction to Widrow-Hoff delta rule
119(2)
3.7.2 Two-layer adaption algorithm
121(1)
3.8 Supervised learning II: Learning in fuzzy systems
122(4)
3.8.1 Introduction
122(1)
3.8.2 Membership function self-tuning
123(1)
3.8.3 Neuro-fuzzy learning systems
124(2)
3.9 Conceptual learning
126(9)
3.9.1 Introduction
126(1)
3.9.2 Knowledge acquisitions through rule learning
127(3)
3.9.3 Combination of neural network and rule learning
130(2)
3.9.4 Rule learning through deep reasoning
132(3)
3.10 Remarks
135(2)
4 System modeling and estimation
137(42)
4.1 Introduction
137(2)
4.2 Math-model based optimal estimation
139(4)
4.3 Math-model free estimation
143(2)
4.4 Fuzzy identification and estimation
145(13)
4.4.1 Introduction
145(1)
4.4.2 Problem formulation
145(1)
4.4.3 Determination of referential fuzzy sets
146(1)
4.4.4 Identification algorithm
147(1)
4.4.5 Use of fuzzy model
148(1)
4.4.6 Self-learning in fuzzy modeling
149(3)
4.4.7 Recursive fuzzy estimator
152(1)
4.4.8 Numerical examples
153(5)
4.5 Application of fuzzy modeling in prediction of product distribution of FCCU
158(4)
4.5.1 Problem statement
158(2)
4.5.2 Fuzzy relational model with self-learning
160(2)
4.5.3 Industrial application results
162(1)
4.6 Pattern recognition modeling and estimation
162(8)
4.6.1 Problem statement
162(1)
4.6.2 Architecture and algorithm
163(4)
4.6.3 Industrial application -- real time quality estimation and control
167(2)
4.6.4 Industrial application result
169(1)
4.7 Neural network modeling and estimation
170(9)
4.7.1 System modeling and estimation for static systems
171(2)
4.7.2 Application for dynamic systems
173(2)
4.7.3 Numerical examples
175(4)
5 Dynamic controls
179(44)
5.1 Review of adaptive control
179(5)
5.1.1 Introduction
179(1)
5.1.2 Problem statement
180(2)
5.1.3 Schemes of parameter adaptive control
182(2)
5.2 Strategic schemes of math-model free dynamic control
184(5)
5.2.1 Problem statement
184(1)
5.2.2 Schemes of parameter adaptive control
185(4)
5.3 Neural network adaptive controls
189(10)
5.3.1 Introduction
189(1)
5.3.2 Problem statement
189(2)
5.3.3 Neural adaptive control with direct feedback
191(8)
5.4 An integrated neural system for coating weight prediction and control
199(13)
5.4.1 Intorduction
199(2)
5.4.2 Problem formulation
201(3)
5.4.3 Integrated neural system for coating weight prediction and control
204(8)
5.5 Fuzzy logic controls
212(4)
5.5.1 Introduction
212(1)
5.5.2 Architecture of FLC
213(2)
5.5.3 Designm of fuzzy-neural adaptive control
215(1)
5.6 Expert control
216(7)
5.6.1 General architecture and functions
216(2)
5.6.2 Aspects in design of an expert control system
218(1)
5.6.3 Working example
219(4)
6 Optimization control techniques
223(46)
6.1 Introduction
223(1)
6.2 Review of on-line optimization control
224(3)
6.2.1 Introduction
224(1)
6.2.2 Math-programming based optimization
225(2)
6.3 Fuzzy optimization control
227(11)
6.3.1 Fuzzy optimization algorithms
227(3)
6.3.2 Fuzzy expert optimization control for FCCU
230(8)
6.4 Dynamic model based expert optimization control
238(9)
6.4.1 System architecture and algorithm
238(2)
6.4.2 Optimization control for a reheating furnace
240(6)
6.4.3 Comments
246(1)
6.5 Expert system for production scheduling
247(7)
6.5.1 Introduction
247(1)
6.5.2 Problem statement
247(1)
6.5.3 Strategies and strcture of development tool
248(5)
6.5.4 Industrial test results
253(1)
6.6 Evolutionary computation
254(11)
6.6.1 Introduction
254(1)
6.6.2 Genetic algorithms
255(2)
6.6.3 Evolutionary algorithms
257(1)
6.6.4 Applications of EAs in job assignment problems
258(2)
6.6.5 Evolutionary neural network training
260(1)
6.6.6 Fuzzy rule learning by GAs
261(4)
6.7 Hopfield network and simulated annealing
265(4)
6.7.1 Hopfield network for combinatorial optimization problems
265(2)
6.7.2 Simulated annealing algorithm
267(2)
7 Multivariate statistics and quality control
269(18)
7.1 Introduction
269(2)
7.1.1 Spc and process control
269(1)
7.1.2 Concepts of SPC
270(1)
7.1.3 Problem statement
270(1)
7.2 Multivariate quality control
271(6)
7.2.1 Problem statement
271(1)
7.2.2 Principal component analysis
272(3)
7.2.3 Partial least-squares regression
275(2)
7.3 PCA using self-organized learning
277(2)
7.4 Nonlinear PLS modeling using neural networks
279(1)
7.5 Industrial applications of PCA and PLS
280(5)
7.5.1 Dimension deduction and data compression using PCA
280(1)
7.5.2 Applications of NNPLS in system modeling and prediction
281(2)
7.5.3 Statistical process control by PCA
283(2)
7.6 On-line quality control
285(1)
7.7 Concluding remarks
286(1)
8 Fault detection and diagnosis
287(16)
8.1 Introduction
287(1)
8.2 Math-model based fault detection and diagnosios
287(2)
8.3 General view of math-model free fault detection and diagnosis
289(2)
8.4 Applications of neutral networks in fault detection and diagnosis
291(5)
8.4.1 General architectures
291(2)
8.4.2 Neural network based mold breakout prediction for steel continuous caster
293(3)
8.5 Fuzzy model based fault detection and diagnosis
296(7)
8.5.1 General architectures
296(1)
8.5.2 Process monitoring for a low pressure chemical vapor deposition
296(7)
Appendix: List of application examples 303(2)
Bibliography 305(10)
Index 315

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