Identification and control of dynamic systems based on least squares wavelet vector machines

Authors:
Jun Li;Jun-Hua Liu
Affiliations:
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China;School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

A novel least squares support vector machines based on Mexican hat wavelet kernel is presented in the paper. The wavelet kernel which is admissible support vector kernel is characterized by its local analysis and approximate orthogonality, and we can well obtain estimates for regression by applying a least squares wavelet support vector machines (LS-WSVM). To test the validity of the proposed method, this paper demonstrates that LS-WSVM can be used effectively for the identification and adaptive control of nonlinear dynamical systems. Simulation results reveal that the identification and adaptive control schemes suggested based on LS-WSVM gives considerably better performance and show faster and stable learning in comparison to neural networks or fuzzy logic systems. LS-WSVM provides an attractive approach to study the properties of complex nonlinear system modeling and adaptive control.