[1]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].智能系统学报编辑部,2015,(04):583-591.[doi:10.3969/j.issn.1673-4785.201412001]
 ZHANG Jiasu,JIANG Yizhang,WANG Shitong.Sparse TSK fuzzy system based on feature selection clustering method[J].CAAI Transactions on Intelligent Systems,2015,(04):583-591.[doi:10.3969/j.issn.1673-4785.201412001]
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基于特征选择聚类方法的稀疏TSK模糊系统(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2015年04期
页码:
583-591
栏目:
出版日期:
2015-08-25

文章信息/Info

Title:
Sparse TSK fuzzy system based on feature selection clustering method
作者:
张佳骕 蒋亦樟 王士同
江南大学 数字媒体学院, 江苏 无锡 214122
Author(s):
ZHANG Jiasu JIANG Yizhang WANG Shitong
School of Digital Media, Jiangnan University, Wuxi 214122, China
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
Keywords:
TSK fuzzy systemfuzzy system dictionaryfuzzy clusteringfeature selectionblock structuresparse representationrules reductionparameter estimation
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201412001
文献标志码:
A
摘要:
为避免模糊系统建模和估计领域的"维数灾难",将TSK(Takagi-Sugeno-Kang)模糊系统建模转换为一个分块稀疏表示问题,提出FCA稀疏TSK模糊系统(FCA-sparse TSK)。首先运用模糊聚类算法(FCA)对样本特征进行化简,并产生模糊系统字典;再利用存在于TSK模糊系统中的分块结构信息,选取重要的模糊规则并对所选模糊规则的后件参数进行估计。该系统同时对模糊规则及模糊规则数进行化简,在合成数据集和真实数据集上都表现出较好的性能。
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-12-01;改回日期:。
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).
作者简介:张佳骕,男,1990年生,硕士研究生,主要研究方向为人工智能与模式识别、模糊系统;蒋亦樟,男,1988年生,博士研究生,主要研究方向为人工智能与模式识别、模糊系统;王士同,男,1964年生,教授,博士生导师,主要研究方向为人工智能、模式识别和生物信息。
通讯作者:张佳骕.E-mail:jiasu0306@163.com.
更新日期/Last Update: 2015-08-28