Modeling and identification for practical nonlinear process using neural fuzzy network–based Hammerstein system

Abstract

To address the strong nonlinearity and unknown disturbance in practical nonlinear process, an identification scheme of neural fuzzy network (NFN)–based Hammerstein nonlinear system using multi-signals is developed in this paper. The proposed Hammerstein system has a static nonlinear subsystem approximated by NFN and a dynamic linear subsystem described by autoregressive exogenous system (ARX). First, the nonlinear subsystem and the linear subsystem are separated and identified by the designed multi-signals, and then parameters of the linear subsystem and noise model are identified simultaneously by making use of recursive extended least squares approach, which is effective for compensating the error caused by output noise. Furthermore, in order to cope with unmeasurable variable issue of the identified system, auxiliary model technology is developed, and the nonlinear subsystem parameters are estimated by applying derived auxiliary model recursive extended least squares approach. Experimental results of three typical nonlinear processes verify the effectiveness and accuracy of the proposed method, and the simulation results show that the proposed method can obtain higher identification accuracy than other optimization algorithms.