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Fuzzy system identification based on support vector regression and genetic algorithm

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2011年第1-2期12卷 50-55页

作者： Li, Wei Yang, Yupu Yang, Zhong Zhang, Changying Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

A new fuzzy identification approach using support vector regression (SVR) and genetic algorithm (GA) is presented in this paper. Firstly positive definite reference function is utilised to construct a qualified Mercer kernel for SVR. Then an improved GA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalisation ability.

关键词： fuzzy system identification positive definite reference function support vector regression SVR genetic algorithm

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A New Fuzzy Identification Approach Using Support Vector Regression and Particle Swarm Optimization Algorithm

A New Fuzzy Identification Approach Using Support Vector Reg...

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： Tian, WenJie Tian, Yue Beijing Union Univ Automat Inst Beijing Peoples R China Capital Univ Econ & Business Finance Inst Beijing Peoples R China

ISBN: (纸本)9781424442461

A new fuzzy identification approach using support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved PSOA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalization ability.

关键词： particle swarm optimization algorithm fuzzy system identification positive definite reference function support vector regression

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A New Fuzzy Identification Approach Using Support Vector Regression and Immune Clone Selection Algorithm

A New Fuzzy Identification Approach Using Support Vector Reg...

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21st Chinese Control and Decision Conference

作者： Tian, WenJie Ai, Lan Geng, Yu Liu, JiCheng BEIJING Union Univ Automat Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781424427239

A new fuzzy identification approach using support vector regression (SVR) and immune clone selection algorithm (ICSA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved ICSA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalization ability.

关键词： Immune clone selection algorithm Fuzzy system identification positive definite reference function TS fuzzy rule Support vector regression

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Fuzzy system identification based on support vector regression and genetic algorithm

Fuzzy system identification based on support vector regressi...

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2008建模、辨识与控制国际会议

作者： Wei Li* Department of Automation, Shanghai Jiao Tong University, Shanghai, China Yupu Yang Department of Automation, Shanghai Jiao Tong University, Shanghai, China Zhong Yang Department of Automation, Shanghai Jiao Tong University, Shanghai, China Changying Zhang Department of Automation, Shanghai Jiao Tong University, Shanghai, China

A new fuzzy identification approach using support vector regression (SVR) and genetic algorithm (GA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved GA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalization ability.

关键词： fuzzy system identification positive definite reference function support vector regression genetic algorithm.

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