Abstract
Based on a novel multidimensional wavelet kernel constructed in Reproducing Kernel Hilbert Space (RKHS), an identification scheme with the Wavelet Support Vector Machine (WSVM) estimator is proposed for nonlinear dynamic systems. The good reproducing property of wavelet kernel function enhances the generalization ability of the system identification scheme. Two cases are presented to validate the proposed method and show its feasibility.
This work was supported by the national 973 key fundamental research project of China under grant 2002CB312200 and national 863 high technology projects foundation of China under grant 2002AA412010.
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Wen, X., Cai, Y., Xu, X. (2005). Wavelet Support Vector Machines and Its Application for Nonlinear System Identification. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_72
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DOI: https://doi.org/10.1007/11427445_72
Publisher Name: Springer, Berlin, Heidelberg
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