Fuzzy Model Identification

Selected Approaches

By: Hans Hellendoorn (Editor), Dimiter Driankov (Editor)

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Paperback | 16 October 1997

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During the past few years two principally different approaches to the design of fuzzy controllers have emerged: heuristics-based design and model-based design. The main motivation for the heuristics-based design is given by the fact that many industrial processes are still controlled in one of the following two ways: - The process is controlled manually by an experienced operator. - The process is controlled by an automatic control system which needs manual, on-line 'trimming' of its parameters by an experienced operator. In both cases it is enough to translate in terms of a set of fuzzy if-then rules the operator's manual control algorithm or manual on-line 'trimming' strategy in order to obtain an equally good, or even better, wholly automatic fuzzy control system. This implies that the design of a fuzzy controller can only be done after a manual control algorithm or trimming strategy exists. It is admitted in the literature on fuzzy control that the heuristics-based approach to the design of fuzzy controllers is very difficult to apply to multiple-inputjmultiple-output control problems which represent the largest part of challenging industrial process control applications. Furthermore, the heuristics-based design lacks systematic and formally verifiable tuning tech niques. Also, studies of the stability, performance, and robustness of a closed loop system incorporating a heuristics-based fuzzy controller can only be done via extensive simulations.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Engineering & Technology Electronics & Communications Engineering Electronics Engineering Automatic Control Engineering Robotics Mathematics Applied Mathematics Fuzzy Set Theory

Graphical & Digital Media Applications 3D Graphics & Modelling Mechanical Engineering & Materials Mechanical Engineering Computer Hardware Energy Technology & Engineering Electrical Engineering

Preface
List of Contributors
Introduction
Fuzzy Identification from a Grey Box Modeling Point of View	p. 3
Constructing Fuzzy Models by Product Space Clustering	p. 53
Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform	p. 91
Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering	p. 121
Fuzzy Identification Using Methods of Intelligent Data Analysis	p. 165
Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN	p. 193
Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms	p. 215
Optimization of Fuzzy Models by Global Numeric Optimization	p. 251
Identification of Linguistic Fuzzy Models Based on Learning	p. 281
Table of Contents provided by Blackwell. All Rights Reserved.

Fuzzy Model Identification

Selected Approaches

At a Glance

Paperback

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How to return your order

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