Identification of non-linear stochastic systems using a new Hammerstein-Wiener neural network: a simulation study through a non-linear hydraulic process

Abouda, Saif Eddine; Abid, Donia Ben Halima; Elloumi, Mourad; Koubaa, Yassine; Châari, Abdelkader

doi:10.1504/ijcat.2020.10031582

Cited by 1 publication

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“…Recently, some novel representation strategies—such as Lipschitz uncertain state space system (Allahverdi et al, 2020), neural networks (Dong et al, 2021; Ma and Yang, 2021), fuzzy systems (Willian et al, 2021; Zangeneh et al, 2020), fuzzy neural network (Zhou et al, 2020), and block-oriented nonlinear systems (Castro et al, 2017; Degachi et al, 2020; Li et al, 2021; Mohsen and Mohammad, 2019; Saif et al, 2020)—have been proposed to improve or construct the nonlinear system. In the literature (Allahverdi et al, 2020), the issues of sensor fault detection and isolation for a class of Lipschitz uncertain nonlinear system were addressed.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Cao

2023

Transactions of the Institute of Measurement and Control

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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.

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