Nonlinear Systems Modeling Using LS-SVM with SMO-Based Pruning Methods

Authors:
Changyin Sun;Jinya Song;Guofang Lv;Hua Liang
Affiliations:
College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China and School of Automation, Southeast University, Nanjing 210096, P.R. China;College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China;College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China;College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

This paper firstly provides a short introduction to least square support vector machine (LS-SVM), then provides sequential minimal optimization (SMO) based on Pruning Algorithms for LS-SVM, and uses LS-SVM to model nonlinear systems. Simulation experiments are performed and indicated that the proposed method provides satisfactory performance with excellent accuracy and generalization property and achieves superior performance to the conventional method based on common LS-SVM and neural networks.