Synchronous Optimization Schemes for Dynamic Systems Through the Kernel-Based Nonlinear Observer Canonical Form

Li, Jimei; Ding, Feng

doi:10.1109/tim.2022.3210952

Cited by 21 publications

(12 citation statements)

References 57 publications

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“…In summary, Equations ( 28)-( 43) consist of the O-AM-HGI algorithm based on the mean value. The O-AM-HGI algorithms can combine some mathematical tools [72][73][74][75] and identification approaches [76][77][78][79][80][81][82] to explore parameter estimation methods of different dynamic stochastic systems [83][84][85][86][87][88][89] and can be applied to signal processing and chemical process control.…”

Section: The O-am-hgi Algorithmmentioning

confidence: 99%

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

Ding,

Xu,

Zhang

et al. 2023

Intl J Robust & Nonlinear

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This article investigates the parameter identification problems of the stochastic systems described by the input‐nonlinear output‐error (IN‐OE) model. This IN‐OE model consists of two submodels, one is an input nonlinear model and the other is a linear output‐error model. The difficulty in the parameter identification of the IN‐OE model is that the information vector contains the unknown variables, which are the noise‐free (true) outputs of the system, the approach taken here is to replace the unknown terms with the outputs of the auxiliary model. Based on the over‐parameterization model and the hierarchical identification principle, an over‐parameterization auxiliary model hierarchical gradient‐based iterative algorithm and an over‐parameterization auxiliary model hierarchical least‐squares‐based iterative algorithm are proposed to estimate the unknown parameters of the IN‐OE systems. Finally, two numerical simulation examples are given to demonstrate the effectiveness of the proposed algorithms.

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Section: The O-am-hgi Algorithmmentioning

confidence: 99%

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

Ding,

Xu,

Zhang

et al. 2023

Intl J Robust & Nonlinear

Self Cite

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show abstract

“…It can solve complex structure, high dimension and large scale system identification, making the sizes of the subsystem problems smaller and simpler than the original problem, thus reducing the calculation of the identification algorithms. [64][65][66] In the following, we develop a Kalman filtering-based hierarchical forgetting factor stochastic gradient algorithm to jointly estimate the parameters and states of the system. The identification model contains the system parameter vector 𝝑 and the noise model parameter vector 𝜽.…”

Section: 2mentioning

confidence: 99%

“…Hierarchical identification is an essential identification method developed based on the decomposition of identification models. It can solve complex structure, high dimension and large scale system identification, making the sizes of the subsystem problems smaller and simpler than the original problem, thus reducing the calculation of the identification algorithms 64‐66 . In the following, we develop a Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm to jointly estimate the parameters and states of the system.…”

Section: System Description and Identification Modelmentioning

confidence: 99%

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

2023

Adaptive Control & Signal

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Summary This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect.

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“…The two-stage recursive gradient identification of Hammerstein nonlinear systems based on the key term separation in this article can integrate some filtering and estimation approaches [89][90][91][92][93][94] to explore identification methods of dynamical stochastic linear and nonlinear systems [95][96][97][98][99][100] and can be applied to engineering fields [101][102][103][104][105][106] such as the information processing systems. The KT-AM-2S-RG algorithm involves the following steps:…”

Section: Kt-am-2s-rg Algorithmmentioning

confidence: 99%

Two‐stage and three‐stage recursive gradient identification of Hammerstein nonlinear systems based on the key term separation

Lv,

Sun,

Pan

2023

Intl J Robust & Nonlinear

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This article explores recursive algorithms for parameter identification issues of Hammerstein output‐error systems. The proposed approach includes the key term separation auxiliary model recursive gradient algorithm, which utilizes the gradient search and the key term separation. To enhance computational efficiency, the system is decomposed into two or three subsystems through the hierarchical identification principle. Based on this, a key term separation based auxiliary model two‐stage recursive gradient algorithm and a key term separation based auxiliary model three‐stage recursive gradient algorithm are presented. The simulation results verify the validity of the obtained algorithms.

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Synchronous Optimization Schemes for Dynamic Systems Through the Kernel-Based Nonlinear Observer Canonical Form

Cited by 21 publications

References 57 publications

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

Two‐stage and three‐stage recursive gradient identification of Hammerstein nonlinear systems based on the key term separation

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