智能系统学报Issue(4)：583-591,9.DOI:10.3969/j.issn.1673-4785.201412001
基于特征选择聚类方法的稀疏 TSK 模糊系统
Sparse TSK fuzzy system based on feature selection clustering method
摘要
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 identifica-tion into a block sparse representation problem.First,FCA -sparseTSK fuzzy system uses the fuzzy clustering algo-rithm ( FCA) to simplify sample features and generate fuzzy system dictionary.Then selects main important fuzzy rules and estimate the fuzzy ruleˊs consequent parameter vector by taking into account the block-structured informa-tion 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.关键词
T-S模糊系统/模糊系统字典/模糊聚类/特征选择/分块结构/稀疏表示/规则约减/参数估计Key words
TSK fuzzy system/fuzzy system dictionary/fuzzy clustering/feature selection/block structure/sparse representation/rules reduction/parameter estimation分类
信息技术与安全科学引用本文复制引用
张佳骕,蒋亦樟,王士同..基于特征选择聚类方法的稀疏 TSK 模糊系统[J].智能系统学报,2015,(4):583-591,9.基金项目
国家自然科学基金资助项目（61272210）；江苏省自然科学基金资助项目（ BK2011417，BK20130155）． （）
