Some Stochastic Gradient Algorithms for Hammerstein Systems with Piecewise Linearity

Pu, Yan; Yang, Yongqing; Chen, Jing

doi:10.1007/s00034-020-01554-z

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…Additional noise is a white noise. The common least-squares based reference model (LS-RM) in [21] and stochastic gradient scheme based auxiliary model (SG-AM) [70] are selected as two comparison algorithms that are designed based on popular prediction error data. The initial values of the three identification methods are listed as follows:…”

Section: Simulation and Experimentsmentioning

confidence: 99%

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

Wang

2022

IEEE Access

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A new robust identification algorithm is introduced in this study for a Hammersteinlike system based on identification error information. With the help of the half-substitution idea, the identification model is converted into a compact model where the coupling parameters are avoided. To reduce the effect of noise signals, a filter gain is proposed to obtain helpful system data. Then, on the basis of the filtered variables and developed forcing variables, the identification error information is extracted from the helpful system data. By using the identification error data, a new parameter estimation adaptive law that differs from the classic prediction error method is derived. Therefore, a new identification scheme framework is proposed and the weakness of the prediction error method is improved. Simulations and a real-life plant are presented to test the validity and practicality of the presented identification approach.The results of parameter estimation and estimation error qualitatively demonstrated the advantages of the proposed algorithm. The computational complexity and performance evaluation indicators results quantitatively indicated that the proposed algorithm produces higher estimation performance compared with the other algorithms.

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Section: Simulation and Experimentsmentioning

confidence: 99%

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

Wang

2022

IEEE Access

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“…The identification strategies of the Hammerstein system are overall classified into different types on the basis of different perspectives. From the perspective of fundamental identification principle, identification approaches mainly include least squares approach (Cheng et al, 2019;Dong et al, 2020;Nejati and Safarinejadian, 2020), stochastic gradient approach (Ji and Kang, 2020;Pu et al, 2020;Shen et al, 2018), orthogonal matching pursuit approach (Wang et al, 2018(Wang et al, , 2019, and maximum likelihood approach (Li et al, 2017c;Wang et al, 2020). Based on the perspective of identification algorithms used, there exist two categories of identification algorithms, namely, the recursive-based algorithm and the iterative-based algorithm.…”

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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“…Parameter estimation of Hammerstein systems is essential for in-depth analysis and study of linear systems (Pan et al, 2018) and nonlinear systems (Avila et al, 2017). There have been many identification methods proposed for Hammerstein systems with single rate (Pu et al, 2021;Zhang et al, 2017). Liu and Bai presented three iterative algorithms to estimate Hammerstein systems with three kinds of nonlinear blocks, infinite impulse response, the saturation and preload (Liu and Bai, 2007).…”

Section: Introductionmentioning

confidence: 99%

Gradient-based Iterative Identification Method for Non-uniformly Sampled Input-nonlinear Systems

Zou,

Xie,

Bai

et al. 2023

CEAI

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This paper proposes an identification method of an input-nonlinear system with saturation and dead-zone nonlinearity using the asynchronous input-output data spaced by non-uniform intervals. The piecewise expression of the nonlinear part is simplified as an analytic function with an available switching function. By extending the traditional continuous linear time-invariant processes to non-uniformly sampled input-nonlinear systems, a concise input-output representation model is derived. Based on the key term separation principle and the auxiliary model identification idea, a gradient-based iterative identification algorithm is developed for simultaneously estimating all parameters of the derived model. Through a numerical example, the proposed algorithm shows its superior estimation accuracy compared to the auxiliary model-based forgetting factor stochastic gradient algorithm. Finally, the application to a mathematical model of a two-tank system indicates the effectiveness of the proposed method.

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Some Stochastic Gradient Algorithms for Hammerstein Systems with Piecewise Linearity

Cited by 6 publications

References 48 publications

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

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

Gradient-based Iterative Identification Method for Non-uniformly Sampled Input-nonlinear Systems

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