Fuzzy model identification

Pao Yue-kong Library Electronic Theses Database

Fuzzy model identification

 

Author: Kong, Wai-chuen
Title: Fuzzy model identification
Degree: M.Sc.
Year: 1998
Subject: Fuzzy systems
System identification
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Electrical Engineering
Pages: [76] leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1437037
URI: http://theses.lib.polyu.edu.hk/handle/200/2502
Abstract: The aim of this report is to present an approach for identifying a fuzzy model composed of fuzzy-logic based linguistic rules for a multi-input/single-output system, in which the conventional mathematical model may fail to give satisfactory results. The proposed approach of identification procedures consist of these two clearly defined steps that are determination of the structure of the model and estimation of the parameters of the model from the data set provided for identification. In the structure identification, we determine the number of rules that is equal to that of fuzzy clusters by some criterions and obtain fuzzy partitions of the input and output spaces by projecting the centroids of the clusters on to the axes of their own co-ordinates. Fuzzy-c-means (FCM) clustering is used to identify the structure of the fuzzy model. In the parameter identification, we formulate a constrained non-linear optimization problem and solve the problem by genetic algorithm (GA) in order to determine the values of the parameters that define the membership functions in the premises and the consequences of fuzzy rules. The proposed approach has been implemented for the identification of Box and Jenkin's gas furnace system as well as for the identification of fuzzy logic controllers for the control of unknown processes. In the identification of gas furnace system, the fuzzy model was identified with high accuracy and small number of fuzzy rules. In the identification of fuzzy logic controller, the identified fuzzy logic controller was effective in setpoint tracking.

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