SYSTEM IDENTIFICATION BASED ON MULTI-KERNEL LEAST SQUARES SUPPORT VECTOR MACHINES (MULTI-KERNEL LS-SVM)

This paper develops a new approach to identify nonlinear systems. A Multi-Kernel Least Squares Support Vector Machine (Multi-Kernel LS-SVM) is proposed. The basic LS-SVM idea is to map linear inseparable input data into a high dimensional linear separable feature space via a nonlinear mapping technique (kernel function) and to carry out linear classification or regression in feature space. The choice of kernel function is an important task which is related to the system nonlinearity degrees. The suggested approach combines several kernels in order to take advantage of their performances. Two examples are given to illustrate the effectiveness of the proposed method.