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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| Published in | Applied soft computing Vol. 2; no. 2; pp. 89 - 103 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
01.12.2002
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| Online Access | Get full text |
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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. |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/S1568-4946(02)00032-7 |