A new approach for identification of MIMO non linear system with RKHS model

In this paper we propose a new approach for the modelling of the multi-variable systems (MIMO) on the Reproducing Kernel Hilbert Space (RKHS). The proposed approach considers the MIMO system as a set of MISO processes modelled in RKHS space. We propose also a comparative study of three identification kernel methods of nonlinear systems modelled in Reproducing Kernel Hilbert Space (RKHS), where the model output results from a linear combination of kernel functions. Theses methods are support vector machines (SVM), regularization networks (RN) and kernel Principal Component Analysis (KPCA). The performances of the proposed MIMO RKHS model and of each kernel method in terms of generalization ability and computing time were evaluated on numerical simulations. Key-Words: Identification, RKHS, SLT, MIMO model, SVM, RN, KPCA