Reconfigurable equilibrium circuit with additional power supply

Zhang, Yu; Huang, Mingming; Wu, Tiezhou; Jiang, Feng

doi:10.1093/ijlct/ctz063

Cited by 26 publications

(12 citation statements)

References 21 publications

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“…The simulation results indicate that the proposed algorithms are effective. What's more, the scientific idea of this work to discuss the problems of parameter estimation for multivariable systems can be expanded to different systems with coloured noises [74,75] and can handle the identification problems of some applied science and engineering [76][77][78][79] such as communication networked systems and power and information systems [80][81][82][83].…”

Section: Discussionmentioning

confidence: 99%

Recursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises

Pan

Zhang

et al. 2020

IET signal process.

152

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By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input-output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective. • This paper decomposes a multivariable output-error autoregressive moving average-like (M-OEARMA-like) system into several subsystems by means of the hierarchical identification principle. • A partially coupled generalised extended projection (PC-GEPJ) algorithm is proposed for M-OEARMA-like system by making use of the coupling identification concept.

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Section: Discussionmentioning

confidence: 99%

Recursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises

Pan

Zhang

et al. 2020

IET signal process.

152

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“…The simulation results show that the proposed algorithms are effective. The proposed methods in this article can combine some mathematical strategies [63][64][65][66] and can be extended to study the filtering, estimation, and prediction problems of different engineering systems with colored noises [67][68][69][70][71][72] and can be applied to other literature studies [73][74][75][76] such as information processing and networked communication systems.…”

Section: Discussionmentioning

confidence: 99%

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

Wang

Zhu

Ding

2020

Adaptive Control & Signal

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This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi-innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.

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“…The proposed maximum likelihood least squares-based iterative methods for output-error bilinear-parameter models with colored noises can combine some mathematical tools [32][33][34][35] and strategies [36][37][38][39] to explore new identification methods of linear and nonlinear stochastic systems [40][41][42] and can be applied to other fields [43][44][45] such as information processing and communication systems, and so on.…”

Section: The Ml-lsi Algorithmmentioning

confidence: 99%

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

Chen

Ding

Lin

et al. 2020

Intl J Robust & Nonlinear

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This article is concerned with the parameter identification of output-error bilinear-parameter models with colored noises from measurement data. An auxiliary model least squares-based iterative method is developed through the overparameterization model. It examines the difficulty of estimating the overparameterized vector, which usually presents a heavy computational burden in the identification process. To overcome this drawback, a parameter separation technique is introduced and the nonlinear model is reformulated as a refined identification model through eliminating the crossmultiplying terms. In this regard, a parameter separation least squares-based iterative (PS-LSI) algorithm is derived by avoiding estimating the redundant parameters. On the basis of the PS-LSI algorithm, we derive a maximum likelihood least squares-based iterative method to further improve the numerical accuracy. The identification is dependent on the formulation of a pseudolinear regression relationship, which contains two linear prefilters constructed from the system and noise models. The performance of this proposed method is confirmed by the numerical simulations as well as direct comparisons with other existing algorithms.

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Reconfigurable equilibrium circuit with additional power supply

Cited by 26 publications

References 21 publications

Recursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises

Recursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

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