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OverviewThis 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. Full Product DetailsAuthor: Hans Hellendoorn , Dimiter DriankovPublisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Edition: Softcover reprint of the original 1st ed. 1997 Dimensions: Width: 15.50cm , Height: 1.80cm , Length: 23.50cm Weight: 0.525kg ISBN: 9783540627210ISBN 10: 3540627219 Pages: 319 Publication Date: 16 October 1997 Audience: College/higher education , Professional and scholarly , Postgraduate, Research & Scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Out of stock ![]() The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available. Table of ContentsGeneral 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.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |