Identification of wiener models using support vector machine

Authors:
Hua Liang;Bolin Wang
Affiliations:
College of Electrical Engineering, Hohai University, Nanjing;College of Electrical Engineering, Hohai University, Nanjing
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

Citing 3
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Abstract

The least squares support vector machines (LS-SVM) regression is presented for the purpose of nonlinear dynamic system identification. LSSVM are used for system identification of Wiener models with memoryless nonlinear blocks and linear dynamical blocks. LS-SVM achieves higher generalization performance. The identification procedure is illustrated using two simulated examples. The results indicate that this approach is effective.