Online modeling of nonlinear systems using improved adaptive kernel methods

Authors:
Xiaodong Wang;Haoran Zhang;Changjiang Zhang;Xiushan Cai;Jinshan Wang;Meiying Ye
Affiliations:
College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Mathematics and Physics, Zhejiang Normal University, Jinhua, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 2
Cited 0

Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Robust nonlinear system identification using neural-network models

IEEE Transactions on Neural Networks

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Abstract

The least squares support vector machines (LS-SVMs) is a kernel method. The training problem of LS-SVMs is solved by finding a solution to a set of linear equations. This makes online adaptive implementation of the algorithm feasible. An improved adaptive algorithm is proposed for training the LS-SVMs in this paper. This algorithm is especially useful on online nonlinear system modeling. The experiments with benchmark problem have shown the validity of the proposed method even in the case of additive noise to the system.