Information Technology Journal1812-56381812-5646Asian Network for Scientific Information10.3923/itj.2006.439.444WuFangfang .ZhaoYinliang .3200653The kernel function of Support Vector Machine (SVM) is an important factor for the learning result of SVM. Based on the wavelet decomposition and conditions of the support vector kernel function, Morlet wavelet kernel function for SVM is proposed. This function is not only a kind of orthonormal function, but also suitable for local signal analysis, signal-noise separation and detection of jumping signals, thus it enhances the generalization ability of the SVM. According to the wavelet kernel function and the regularization theory, Least squares support vector machine on Morlet wavelet kernel function (LS-MWSVM) is proposed to greatly simplify the solving process of MWSVM. The LS-MWSVM is then applied to the nonlinear system identification to test the validity of the Morlet wavelet kernel function. Computer simulations show that the modeling ability is improved and computation burden is alleviated, comparing with LS-SVM whose kernel function is Gaussian function.]]>Scholkopf, B., K.K. Sung, C.J.C. Burges, F. Girosi, P. Niyogi, T. Poggio and V. Vapnik,19974527582765Burges, C.J.C.,19982121167Burges, C.J.C.,19991999pp: 89-116pp: 89-116Osuna, E., R. Freund and F. Girosit,19971997pp: 130136Mercer, J.,1909A209415446Smola, A.,199811637649Suykens, J.A.K. and J. Vandewalle,19999293300Vapnik, V.,1995pp: 1-175pp: 1-175Zhang, Q. and A. Benveniste,19923889898Zhang, X.G.,2000263242Zhang, L., Z. Weida and J. Licheng,2004343439