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2003 | OriginalPaper | Chapter

Fuzzy Model Identification

Author : János Abonyi

Published in: Fuzzy Model Identification for Control

Publisher: Birkhäuser Boston

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Abstract Fuzzy model identification is an effective tool for the approx- imation of uncertain nonlinear systems on the basis of measured data. The identification of a fuzzy model using input-output data can be divided into two tasks: structure identification, which determines the type and number of the rules and membership functions, and parameter identification. For both structural and parametric adjustment, prior knowledge plays an im- portant role. Hence, in this book the rules of the fuzzy system are designed based on the available a priori knowledge and the parameters of the mem- bership, and the consequent functions are adapted in a learning process based on the available input-output data. Hence, this chapter is devoted mainly to the parameter identification of the proposed fuzzy models, but certain structure identification tools are also discussed.

Metadata
Title
Fuzzy Model Identification
Author
János Abonyi
Copyright Year
2003
Publisher
Birkhäuser Boston
DOI
https://doi.org/10.1007/978-1-4612-0027-7_4