TSK Fuzzy Modeling Based on Kernelized fuzzy clustering and Least Squares Support Vector Machines

被引：0
|
作者：
Liu, Wei [1 ]
机构：
[1] Guangdong Univ Technol, Fac Appl Math, Guangzhou 510090, Guangdong, Peoples R China
来源：
2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL IV, PROCEEDINGS | 2009年
关键词：
D O I：
10.1109/AICI.2009.177
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a novel learning method based on kernelized fuzzy clustering and least squares support vector machines (LSSVM) is presented to improve the generalization ability of a Takagi-Sugeno-Kang (TSK) fuzzy modeling. Firstly, the fuzzy partition of the product space of input and output is obtained by kernelized fuzzy clustering. Then, a computationally efficient numerical method is proposed. In the proposed algorithm, the fuzzy kernel is generated by premise membership functions. Numerical experiments show that the presented algorithm improves the generalization ability and robustness of TSK fuzzy models compared with traditional learning methods and LSSVM
引用
收藏
页码：133 / 137
页数：5
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