Identification of chaotic system using Hammerstein-ELM model

Abstract

In this paper, a new method is proposed to identify chaotic system based on Hammerstein-extreme learning machine (Hammerstin-ELM) model. Hammerstein-ELM model consists of a static nonlinear function followed by a linear dynamic part. The static nonlinear function is represented by a ELM neural network. A generalized ELM algorithm is presented to simultaneously identify the parameters of linear dynamic part and those of ELM neural networks. Numerical examples demonstrate the effectiveness of the proposed method.