A hybrid clustering and gradient descent approach for fuzzy modeling

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Title:	A hybrid clustering and gradient descent approach for fuzzy modeling
Authors:	翁慶昌;Wong, Ching-chang;Chen, C.C.
Contributors:	淡江大學電機工程學系
Keywords:	Fuzzy systems;Parameter estimation;Clustering algorithms;Fuzzy sets;Mathematical model;Clustering methods;Nonlinear systems;Inference algorithms;Uncertain systems;System identification
Date:	1999-12
Issue Date:	2011-10-15 00:49:24 (UTC+8)
Abstract:	In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach.
Relation:	IEEE transactions on systems, man and cybernetics 29(6), pp.686-693
DOI:	10.1109/3477.809024
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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