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Fuzzy Model Identification: Selected Approaches by Hans Hellendoorn at Abbey's Bookshop,
Fuzzy Model Identification: Selected Approaches
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Hans Hellendoorn Dimiter Driankov

Fuzzy Model Identification

Selected Approaches

Hans Hellendoorn,  Dimiter Driankov

$130.95   $105.07

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English
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
16 October 1997
Fuzzy set theory
This carefully edited collection of recent works in fuzzy model identification opens the field to conventional control theorists as a complement to existing approaches, provides practicing engineers with new techniques, and emphasizes opportunities for new theory by bringing together different methods to identify the same types of fuzzy models. In control engineering, mathematical models are often constructed without using system data (white-box models) or using data but no insight (black-box models). The authors in this volume combine white- and black-box models chosen from types of structures known to be flexible and successful in applications. They use the same notation and terminology, and each describes a model with an identification technique and gives a practical example to show how the method works.
Edited by:   ,
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Country of Publication:   Germany
Edition:   Softcover reprint of the original 1st ed. 1997
Dimensions:   Height: 235mm,  Width: 155mm,  Spine: 18mm
Weight:   525g
ISBN:   9783540627210
ISBN 10:   3540627219
Pages:   319
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Further / Higher Education ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
General Overview.- Fuzzy Identification from a Grey Box Modeling Point of View.- Clustering Methods.- Constructing Fuzzy Models by Product Space Clustering.- Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform.- Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering.- Neural Networks.- Fuzzy Identification Using Methods of Intelligent Data Analysis.- Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN.- Genetic Algorithms.- Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms.- Optimization of Fuzzy Models by Global Numeric Optimization.- Artificial Intelligence.- Identification of Linguistic Fuzzy Models Based on Learning.

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