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

Pan, Jian; Ma, Hao; Zhang, Xiao; Liu, Qinyao; Ding, Feng; Chang, Yufang; Sheng, Jie

doi:10.1049/iet-spr.2019.0481

Cited by 152 publications

(90 citation statements)

References 83 publications

(130 reference statements)

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“…The simulation results validate the performance of the presented algorithms. The algorithm in this article is proposed for linear time-delay systems but the idea can extended to other linear and nonlinear time-delay stochastic systems [65][66][67][68][69][70][71][72][73][74][75] and can be applied to other literature studies [76][77][78][79][80][81][82][83][84][85][86][87][88] such as signal processing and vibration analysis.…”

Section: Discussionmentioning

confidence: 99%

Parameter estimation for time‐delay systems based on the frequency responses and harmonic balance methods

Ding

et al. 2020

Adaptive Control & Signal

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Two parameter estimation methods for linear time-delay systems are proposed based on the frequency responses and the harmonic balance methods. One is the stochastic gradient gradient-based iterative (SG-GI) algorithm and the other is the recursive least squares gradient-based iterative (RLS-GI) algorithm. These two methods can estimate the unknown parameters and the unknown time delays simultaneously by giving sinusoidal signals with different angular frequencies. Furthermore, a comparative study reveals that the RLS-GI algorithm is more effective than the SG-GI algorithm. The effectiveness of the proposed algorithms is illustrated by a numerical example.

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Section: Discussionmentioning

confidence: 99%

Parameter estimation for time‐delay systems based on the frequency responses and harmonic balance methods

Ding

et al. 2020

Adaptive Control & Signal

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“…The proposed algorithms in this article are based on this identification model in (10). Many identification methods are derived based on the identification models of the systems [19][20][21][22][23][24][25] and can be used to estimate the parameters of other linear systems and nonlinear systems [26][27][28][29][30][31][32] and can be applied to fields such as chemical process control systems. Remark 1.…”

Section: System Description and Identification Modelmentioning

confidence: 99%

Variational Bayesian identification for bilinear state space models with Markov‐switching time delays

Fei

Xiong

et al. 2020

Intl J Robust & Nonlinear

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This article studies the parameter identification problem for bilinear state space models with time-varying time delays. Considering the correlation of time delays, the Markov chain switching mechanism is adopted to model the delay sequence. Based on the observer canonical form, the bilinear state space model is transformed into a regressive form. A bilinear state observer is designed to estimate the state variables. Under the variational Bayesian scheme, the system parameters, discrete delays, and the Markov transition probabilities are identified by using the measurement data. A numerical example and a continuous stirred tank reactor simulation are employed to validate the effectiveness of the proposed algorithm.

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“…The proposed algorithms in this article are based on this identification model. Many identification methods are derived based on the identification models of the systems [32][33][34][35] and can be used to estimate the parameters of other linear systems and nonlinear systems [36][37][38][39] and can be applied to other fields [40][41][42][43] such as chemical process control systems.…”

Section: Problem Statementmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Ding

et al. 2020

Adaptive Control & Signal

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Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.

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

Cited by 152 publications

References 83 publications

Parameter estimation for time‐delay systems based on the frequency responses and harmonic balance methods

Parameter estimation for time‐delay systems based on the frequency responses and harmonic balance methods

Variational Bayesian identification for bilinear state space models with Markov‐switching time delays

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

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