Reference : Identification of the linear parts of nonlinear systems for fuzzy modeling
Scientific journals : Article
Business & economic sciences : Quantitative methods in economics & management
Identification of the linear parts of nonlinear systems for fuzzy modeling
Rezaei Sadrabadi, Mahmood mailto [Eindhoven University of Technology > Mathematics and Computer Science > > >]
Applied Soft Computing
Yes (verified by ORBi)
[en] Fuzzy clustering ; fuzzy modeling ; structure identification ; nonlinear functions
[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.
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