Article (Scientific journals)
Identification of the linear parts of nonlinear systems for fuzzy modeling
Rezaei Sadrabadi, Mahmood
2011In Applied Soft Computing, 11, p. 807-819
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Keywords :
Fuzzy clustering; fuzzy modeling; structure identification; nonlinear functions
Abstract :
[en] In direct approach to fuzzy modeling, structure identification is one of the most critical tasks. In modeling the nonlinear system, this fact is more crucial. In this paper, a new hybrid method is proposed to cluster the data located in the linear parts on the nonlinear systems. The proposed method can partition the input–output data in two groups: data located in the linear parts and data in the extrema. It is shown that the first group of data is suitable to be clustered by Fuzzy C-Regression Model (FCRM) clustering algorithm and the second group by Fuzzy C-Means (FCM). Then, based on the above findings, a new hybrid clustering algorithm is proposed. Finally, the proposed approach is tested and validated by several numerical examples of nonlinear functions.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Rezaei Sadrabadi, Mahmood ;  Eindhoven University of Technology > Mathematics and Computer Science
Language :
English
Title :
Identification of the linear parts of nonlinear systems for fuzzy modeling
Publication date :
January 2011
Journal title :
Applied Soft Computing
ISSN :
1568-4946
eISSN :
1872-9681
Publisher :
Elsevier, Netherlands
Volume :
11
Pages :
807-819
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 19 November 2010

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