[1]ZHANG Jiasu,JIANG Yizhang,WANG Shitong.Sparse TSK fuzzy system based on feature selection clustering method[J].CAAI Transactions on Intelligent Systems,2015,10(4):583-591.[doi:10.3969/j.issn.1673-4785.201412001]
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Sparse TSK fuzzy system based on feature selection clustering method
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
10
Number of periods:
2015 4
Page number:
583-591
Column:
学术论文—智能系统
Public date:
2015-08-25
- Title:
- Sparse TSK fuzzy system based on feature selection clustering method
- Keywords:
- TSK fuzzy system; fuzzy system dictionary; fuzzy clustering; feature selection; block structure; sparse representation; rules reduction; parameter estimation
- CLC:
- TP391
- DOI:
- 10.3969/j.issn.1673-4785.201412001
- Abstract:
- In order to solve the curse of dimensionality existing in fuzzy system identification and approximation, this paper proposes the FCA-sparseTSK fuzzy system by casting the Takagi-Sugeno-Kang(TSK ) fuzzy system identification into a block sparse representation problem. First, FCA-sparseTSK fuzzy system uses the fuzzy clustering algorithm (FCA) to simplify sample features and generate fuzzy system dictionary. Then selects main important fuzzy rules and estimate the fuzzy rule’s consequent parameter vector by taking into account the block-structured information that exists in the TSK fuzzy model. The FCA-sparseTSK fuzzy system simplifies the fuzzy rules and the number of fuzzy rules at the same time and shows good performance in artificial datasets and real-world datasets.
- References:
-
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Last Update:
2015-08-28