A nonlinear MIMO system identification based on improved Multi-Kernel Least Squares Support Vector Machines (Improved Multi-Kernel LS-SVM)

In this paper, a new method for the identification of nonlinear Multiple Input-Multiple Output (MIMO) systems is proposed. An improved Multi-Kernel Least Squares Support Vector Machines (Improved Multi-Kernel LS-SVM) based on Constrained Particle Swarm Optimization (CPSO) is given. 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 and the corresponding parameters 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. The CPSO technique is used to give solution for the determination of optimized kernel parameters and their evolved weights. Simulation results show that the CPSO can quickly obtain the optimal parameters and therefore satisfying the required precision.