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A clustering algorithm for fuzzy model identification
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DOI：10.1016/S0165-0114(96)00384-3.png)
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Abstract
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The fuzzy model proposed by Takagi and Sugeno can represent highly nonlinear systems and is widely used for the representation of fuzzy rules. In this paper, the model is firstly modified to make its identification easier. Based on the fuzzy c-partition space, four criteria are proposed for optimization of the model parameters. Following that, a clustering algorithm composed of fuzzy c-linear functions clustering and like fuzzy c-means clustering is developed for minimizing the four criteria. An identification scheme for rule's premise and consequence parameters is deduced from the clustering algorithm in succession. Finally, four examples are demonstrated to verify the effectiveness of the proposed algorithm. (C) 1998 Elsevier Science B.V. All rights reserved.
Keywords:
cluster analysis
linguistic modeling
nonlinear system identification
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