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Application to nonlinear control using least squares wavelet support vector machines
Author(s): 
Pages: 620-625
Year: Issue:  4
Journal: Electric Machines and Control

Keyword:  support vector machinesleast square support vector machineswavelet kernelCholesky algorithmnonlinear dynamical systemsadaptive control;
Abstract: A form of least squares wavelet support vector machines(LS-WSVM) using multi-dimensional admissible wavelet kernel was proposed, which combined the wavelet techniques with support vector machines(SVM). The wavelet kernel was characterized by its local analysis and approximate orthogonality. Simultaneously, an efficient implementation algorithm via Cholesky factorisation for LS-WSVM was also given. The LS-WSVM was then applied to adaptive control of nonlinear dynamical systems. Simulation results reveal that the modeling and adaptive control scheme suggested based on LS-WSVM gives considerably better performance and shows faster and stable learning in comparison with neural networks or fuzzy logic systems. Furthermore the approximation accuracy of the LS-WSVM one order of magnitude increases over the LS-SVM under the same conditions. The proposed LS-WSVM method for the adaptive control of nonlinear dynamical systems shows the effectiveness and applicability.
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