Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

Li, Feng; Zhu, Xun; Cao, Qingfeng

doi:10.1007/s00034-022-02240-y

Cited by 10 publications

(2 citation statements)

References 40 publications

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“…In the last 10 years, the application of the H-W model for the identification and estimation of parameters of nonlinear systems has mainly used classical functions for input and output nonlinearities. In [35], the authors propose a new solution to estimate the parameters of the Hammerstein nonlinear model using a multi-signal approach. A novel Adaptive Dual Nonlinear Model Predictive Control (ADNMPC) based on discrete-time block-oriented models is developed [36].…”

Section: Discussionmentioning

confidence: 99%

“…The results obtained by the developed method using the improved almost orthogonal Müntz-Legendre polynomials in the H-W model have been compared with those obtained by the the iterative least squares and a recursive least squares [1], the method using the Genetic Algorithm (GA) combined with the Recursive Least Squares (RLS) method [4], and the method for estimating the parameters of the Hammerstein nonlinear model using a multi-signal approach [35]. The results are shown in Table II.…”

Section:  mentioning

confidence: 99%

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Identification of Nonlinear Systems Using the Hammerstein-Wiener Model with Improved Orthogonal Functions

Nikolić

Milovanović

Dankovic

et al. 2023

ELEKTRON ELEKTROTECH

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Hammerstein-Wiener systems present a structure consisting of three serial cascade blocks. Two are static nonlinearities, which can be described with nonlinear functions. The third block represents a linear dynamic component placed between the first two blocks. Some of the common linear model structures include a rational-type transfer function, orthogonal rational functions (ORF), finite impulse response (FIR), autoregressive with extra input (ARX), autoregressive moving average with exogenous inputs model (ARMAX), and output-error (O-E) model structure. This paper presents a new structure, and a new improvement is proposed, which is consisted of the basic structure of Hammerstein-Wiener models with an improved orthogonal function of Müntz-Legendre type. We present an extension of generalised Malmquist polynomials that represent Müntz polynomials. Also, a detailed mathematical background for performing improved almost orthogonal polynomials, in combination with Hammerstein-Wiener models, is proposed. The proposed approach is used to identify the strongly nonlinear hydraulic system via the transfer function. To compare the results obtained, well-known orthogonal functions of the Legendre, Chebyshev, and Laguerre types are exploited.

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Section: Discussionmentioning

confidence: 99%

Section:  mentioning

confidence: 99%

Identification of Nonlinear Systems Using the Hammerstein-Wiener Model with Improved Orthogonal Functions

Nikolić

Milovanović

Dankovic

et al. 2023

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

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Parameter learning of multi‐input multi‐output Hammerstein system with measurement noises utilizing combined signals

Li,

Sun,

Cao

2024

Adaptive Control & Signal

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SummaryIn this article, the parameter learning scheme for the multi‐input multi‐output (MIMO) Hammerstein nonlinear systems under measurement noises is studied, which is derived by exploiting the correlation analysis and data filtering technique. The coupled MIMO Hammerstein system presented involves a static nonlinear subsystem modeled by neural fuzzy model (NFM), and a dynamic linear subsystem established by autoregressive moving average with extra input (ARMAX) model. To learn the unknown parameter of the MIMO Hammerstein system, the combined signals are designed to realize that identification of the nonlinear subsystem is separated from that of linear subsystem. First, the correlation properties of separable signals in a nonlinear system are analyzed, then the parameters of the linear subsystem are estimated utilizing correlation analysis, which can deal with the issue of unmeasured intermediate variable in the Hammerstein system. Second, the data filtering technique is introduced to derive the data filtering‐based recursive least squares technique for learning the nonlinear subsystem parameter, which can reduce the impact of the moving average noise and improve the precision of parameter estimation. Finally, the effectiveness and feasibility of the proposed identification scheme is proved by numerical simulation and nonlinear pH process.

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Parameter Identification for the Hammerstein-Wiener Nonlinear Time Delay Systems with Process Noises

Li,

Han,

et al. 2024

Circuits Syst Signal Process

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Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

Cited by 10 publications

References 40 publications

Identification of Nonlinear Systems Using the Hammerstein-Wiener Model with Improved Orthogonal Functions

Identification of Nonlinear Systems Using the Hammerstein-Wiener Model with Improved Orthogonal Functions

Parameter learning of multi‐input multi‐output Hammerstein system with measurement noises utilizing combined signals

Parameter Identification for the Hammerstein-Wiener Nonlinear Time Delay Systems with Process Noises

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