Identification of MIMO Hammerstein Models Using Support Vector Machine

Authors:
Hua Liang;Bolin Wang
Affiliations:
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, Part III
Year:
2007

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Abstract

The least squares support vector machines (LS-SVMs) regression is presented for the purpose of nonlinear dynamic system identification. LS-SVMs are used for system identification of Hammerstein models with memoryless nonlinear blocks and linear dynamical blocks. LS-SVMs achieves higher generalization performance than a hybrid neural network (HNN) which consist of a multi-layer feed-forward neural network (MFNN) in cascade with a linear neural network (LNN). The identification procedure is illustrated using two simulated examples. The results indicate that this approach is effective even in the case of additive noise to the system.