Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory

Ma, Ping; Wang, Lei

doi:10.1002/acs.3302

Cited by 73 publications

(38 citation statements)

References 77 publications

(82 reference statements)

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“…The simulation results demonstrate the effectiveness of the proposed algorithms and show that the F-MI-RGLS algorithm can obtain higher parameter estimation accuracy and provide reliable model predictions. The proposed approaches in the article can combine other identification methods [105][106][107][108][109][110][111][112] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises [113][114][115][116][117][118][119][120] and can be applied to literatures [121][122][123][124][125][126][127][128] such as paper-making systems, information processing, engineering systems, and so on.…”

Section: Discussionmentioning

confidence: 99%

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Fan

Liu

2022

Optim Control Appl Methods

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Piecewise-linear nonlinearity is an effective representation for hard nonlinearities such as saturation, dead zone, and backlash, which can be also used as a general tool for approximating nonlinear characteristics. For the input piecewise-linear output-error autoregressive systems, we develop a parametric expression of the piecewise-linear nonlinearity through a switching function and position functions, and derive the identification model of the system by using the key item separation. Based on the optimization criterion, an auxiliary model-based multi-innovation recursive generalized least-squares algorithm is deduced for estimating the unknown parameters according to the obtained model. Since the system is disturbed by colored noise, we introduce the data filtering technique from a view point of improving the parameter estimation accuracy. The filtering identification model of the system is derived and a filtering-based multi-innovation recursive generalized least-squares algorithm is proposed. The simulation example demonstrates the effectiveness of the proposed algorithms and shows that the filtering-based multi-innovation recursive generalized least-squares algorithm has better identification performance.

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Section: Discussionmentioning

confidence: 99%

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Fan

Liu

2022

Optim Control Appl Methods

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“…The simulation examples show the merits and effectiveness of the proposed algorithms. In addition, The proposed approaches in the article can combine other mathematical tools and statistical strategies and identification methods [89][90][91][92][93][94] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises and can be applied to literatures [95][96][97][98][99][100][101][102] such as paper-making systems, information processing, transportation communication systems [103][104][105][106][107][108][109][110][111] and so on.…”

Section: Discussionmentioning

confidence: 99%

Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

Jiang

Wan

2021

Intl J Robust & Nonlinear

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This article deals with the parameter identification of the generalized time-varying systems. The time-varying parameter vector can be expressed as a coefficient matrix multiplied by a measurable disturbance vector, the common identification methods cannot be used to estimate the parameters of the generalized time-varying systems directly. This motivates us to develop new iterative identification algorithms. The gradient-based iterative (GI) algorithm is proposed by means of the iterative technique. Moreover, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. The MDW GI algorithm is proposed to improve the parameter estimation accuracy. The numerical simulation is provided and the simulation results show that the proposed identification methods are effective for the generalized time-varying systems.

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“…The proposed parameter estimation algorithms in this article are based on this identification model. Many identification methods are derived based on the identification models of the systems [37][38][39][40] and these methods can be used to estimate the parameters of other linear systems and nonlinear systems [41][42][43][44] and can be applied to other fields [45][46][47][48][49][50] such as chemical process control systems. There exists the product of the parameter vector b of the CAR model in the forward channel and c of the nonlinear block in the feedback channel in (7) such that the identification model is a typical bilinear-in-parameter model.…”

Section: System Descriptionmentioning

confidence: 99%

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Wei

Xiao

et al. 2022

Intl J Robust & Nonlinear

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This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive least squares algorithm is developed to handle the difficulty of the bilinear‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate. Furthermore, the convergence analysis of the proposed algorithms are established by means of the stochastic process theory. The effectiveness of the proposed algorithms are illustrated by the simulation example.

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Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory

Cited by 73 publications

References 77 publications

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

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