TSK Fuzzy Model Base on Least-Squares Support Vector Machines

Journal of South China University of Technology (Natural Science Edition) ›› 2009, Vol. 37 ›› Issue (5): 130-134.

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TSK Fuzzy Model Base on Least-Squares Support Vector Machines

Cai Qian-feng¹ Hao Zhi-feng² Yang Xiao-wei³

1 School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong, China; 2. Faculty of Computer, Guangdong University of Technology, Guangzhou 510090, Guangdong, China; 3. School of Science, South China University of Technology, Guangzhou 510640, Guangdong, China

Received:2008-06-17 Revised:2008-08-03 Online:2009-05-25 Published:2009-05-25
Contact: 蔡前凤（1973-），女，在职博士生，广东工业大学讲师，主要从事模糊系统、支持向量机、人工智能等研究． E-mail:caiqianfeng@163．com
About author:蔡前凤（1973-），女，在职博士生，广东工业大学讲师，主要从事模糊系统、支持向量机、人工智能等研究．
Supported by:
国家自然科学基金资助项目（60433020,10471045）;广东省科技计划项目（2008肋80701005）;信息安全国家重点实验室开放课题基金资助项目（04-01）;广东工业大学青年基金资助项目（062056）;惠州市技术研究与开发资金项目（08-117）

Abstract

Abstract:

In order to improve the generalization capability of Takagi-Sugeno-Kang （TSK） fuzzy model in high-di- mension space, a novel algorithm of TSK model is proposed based on the structural risk minimization principle. In this algorithm, first, the antecedent membership functions of fuzzy rules are obtained by means of the Gustafson- Kessel （GK） algorithm. Next, the consequent parameters of fuzzy rules are determined by using the least-square support vector regression （LSSVR） machine. Then, the kernel function of LSSVR is deduced by the antecedent membership functions of fuzzy rules and is proved to be a Mercer kernel. Experimental results show that the pro- posed algorithm has better generalization capability than the conventional techniques of TSK model and is more ro- bust than LSSVR.

Key words: fuzzy system, fuzzy rule, fuzzy clustering, support vector machine, kernel function

Cai Qian-feng Hao Zhi-feng Yang Xiao-wei. TSK Fuzzy Model Base on Least-Squares Support Vector Machines[J]. Journal of South China University of Technology (Natural Science Edition), 2009, 37(5): 130-134.

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TSK Fuzzy Model Base on Least-Squares Support Vector Machines

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