Kernel‐based regularization least squares algorithm for nonlinear time‐delayed systems using self‐organizing maps

Abstract

AbstractThis article proposes a kernel‐based regularization least squares algorithm for nonlinear time‐delayed systems with unknown structure. Since the structure and the time‐delay of the model are unknown, a model pool constituted of several Volterra series is constructed with the aim of approximating the nonlinear model which has different time‐delays. Then, a self‐organizing maps method and a kernel‐based regularization method are interactively used to update the time‐delay and parameters. Compared with the traditional algorithms, the proposed algorithm has no limitation on the nonlinear model, and can describe the dynamics of the nonlinear model with a simple structure. Finally, simulation examples are given to show the effectiveness of the algorithm.