Study of on-line weighted least squares support vector machines

Authors:
Xiangjun Wen;Xiaoming Xu;Yunze Cai
Affiliations:
Automation Department, Shanghai Jiaotong University, Shanghai, China;Automation Department, Shanghai Jiaotong University, Shanghai, China;Automation Department, Shanghai Jiaotong University, Shanghai, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Based on rolling optimization method and on-line learning strategies, a novel weighted least squares support vector machines (WLS-SVM) are proposed for nonlinear system identification in this paper. The good robust property of the novel approach enhances the generalization ability of LS-SVM method, and a real world nonlinear time-variant system is presented to test the feasibility and the potential utility of the proposed method.