Design of adaptive Takagi–Sugeno–Kang fuzzy models

The paper describes a method of fuzzy model generation using numerical data as a starting point. The algorithm generates a Takagi-Sugeno-Kang fuzzy model, characterised with transparency, high accuracy and small number of rules. The training algorithm consists of three steps: partitioning of the inp...

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Bibliographic Details
Published in:Applied soft computing 2002-12, Vol.2 (2), p.89-103
Main Author: Kukolj, Dragan
Format: Article
Language:English
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Summary:The paper describes a method of fuzzy model generation using numerical data as a starting point. The algorithm generates a Takagi-Sugeno-Kang fuzzy model, characterised with transparency, high accuracy and small number of rules. The training algorithm consists of three steps: partitioning of the input-output space using a fuzzy clustering method; determination of parameters of the consequent part of a rule from over-determined batch least-squares (LS) formulation of the problem, using singular value decomposition algorithm; and adaptation of these parameters using recursive least-squares method. Three illustrative well-known benchmark modelling problems serve the purpose of demonstrating the performance of the generated models. The achievable performance is compared with similar existing models, available in literature.
ISSN:1568-4946
DOI:10.1016/S1568-4946(02)00032-7