Parameter Identification of Nonlinear Systems with Time-delay Based on the Multi-innovation Stochastic Gradient Algorithm

Abstract

This paper considers the parameter identification problem of block-oriented Hammerstein nonlinear systems with time-delay. Firstly, we adopt the data filtering technique to transform the identification model so that all the parameters will be separated in the resulting identification model which has no redundant parameters. Secondly, a multi-innovation stochastic gradient algorithm is used to estimate the system parameters. The proposed method has high computational efficiency and good accuracy. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.