Application of Adaptive Least Square Support Vector Machines in Nonlinear System Identification

Xiaodong Wang Xiaodong Wang,G Lv,Weifeng Liang Weifeng Liang,Xiushan Cai Xiushan Cai,Haoran Zhang Haoran Zhang,Changjiang Zhang Changjiang Zhang

doi:10.1109/wcica.2006.1712685

Application of Adaptive Least Square Support Vector Machines in Nonlinear System Identification

Xiaodong Wang Xiaodong Wang, G Lv + Show 4 more

https://doi.org/10.1109/wcica.2006.1712685

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Publication Date: Jan 1, 2006

Citations: 11

Affiliation: Zhejiang Normal University

#Least Squares Support Vector Machine #Variant Of Support Vector Machine + Show 8 more

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Abstract

Training problem of least squares support vector machine (LS-SVM) is solved by finding a solution to a set of linear equations. This makes online adaptive implementation of the algorithm feasible. In this paper, an adaptive algorithm for the purpose of nonlinear system identification is proposed. Using this training algorithm, a variant of support vector machine has been developed called adaptive LS-SVM. The adaptive LS-SVM is especially useful on online system identification. Several pertinent numerical simulations have shown the validity of the proposed method

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