Regularization for Nonlinear System Identification

Abstract In this chapter we review some basic ideas for nonlinear system identification. This is a complex area with vast and rich literature. One reason the richness that very many parameterizations of unknown have been suggested, each various proposed estimation methods. We will first describe details nonparametric techniques based on Reproducing Kernel Hilbert Space theory Gaussian regression. The focus be use regularized least squares, equipped or polynomial kernel. Then, new kernel able to account features dynamic systems, including fading memory concepts. Regularized Volterra models also discussed. then provide brief overview neural deep networks, hybrid systems identification, block-oriented like Wiener Hammerstein, parametric variable selection