Title :
Least-squares auto-correlation wavelet kernel method for regression estimation
Author :
Xing, Yongzhong ; Wu, Xiaobei ; Xu, Zhiliang ; Cheng, Qi
Author_Institution :
Nanjing Univ. of Sci. & Technol., Nanjing
Abstract :
Based on the auto-correlation wavelet kernel, a novel notion of least squares support vector machine (LS-SVM) with universal auto-correlation wavelet kernels is proposed for approximating arbitrary nonlinear functions. The translation invariant property of the auto-correlation wavelet kernel function enhances the generalization ability of the LS-SVM method and some experimental results are included to demonstrate the efficiency and validity of the proposed method.
Keywords :
function approximation; least squares approximations; nonlinear functions; regression analysis; support vector machines; wavelet transforms; LS-SVM method; arbitrary nonlinear function approximation; least squares support vector machine; least-squares autocorrelation wavelet kernel method; regression estimation; Autocorrelation; Kernel; Lagrangian functions; Multidimensional systems; Nonlinear dynamical systems; Nonlinear systems; Signal processing; System identification; Auto-correlation wavelet kernel; Function regression; LS-SVM;
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
DOI :
10.1109/CCDC.2008.4598186
