The model equivalence based parameter estimation methods for Box–Jenkins systems

Ding, Feng; Meng, Dandan; Wang, Qi

doi:10.1016/j.jfranklin.2015.08.018

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…The following step is to estimate the feedback channel parameters. According to the model equivalence principle [25], and from (2), we haveŜ =ηθ c , wherê…”

Section: Identification Algorithmmentioning

confidence: 99%

“…For a sampling data length m = 300 and the noise variance σ 2 = 0.10 2 , using the proposed Th-BPDN algorithm to estimate the sparse vector with the first 150 data, and using the remaining 150 data to validate the model, the parameter estimation vector iŝ θ = [−0.1960, 0.2913, 0 93 , 3.4841, 3.9644, 0.8180, 0 27 , 3.4622, 2.8900, 0 73 ] T ∈ R 200 , (25) and the root mean square error of the output is…”

Section: Simulation Examplementioning

confidence: 99%

“…As can be seen, the predicted outputsŷ(t) are very close to the true outputs y(t), and their errors are close to zero. From (25), it can be seen that the numbers of zeros in each zero-block are z 1 = 93, z 2 = 27 and z 3 = 73. Then, according to (23), the estimated time-delays can be computed bŷ…”

Section: Simulation Examplementioning

confidence: 99%

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A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

Chen¹,

Liu

Chen

et al. 2020

IEEE Access

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This paper presents a novel method to identify a class of closed-loop systems, in which both the forward channel and the feedback channel have unknown time-delays. Taking into account the time-delays, an overparameterized identification model with a sparse parameter vector is established. Based on the basis pursuit de-noising criterion, the sparse parameter vector is estimated by solving a quadratic programming. The time-delays and the parameters are estimated according to the structure of the parameter estimation vector and the model equivalence principle, respectively. The proposed method is applicable even in the case of a few number of sampled data. The effectiveness of the proposed algorithm is verified by the numerical simulation results. INDEX TERMS Closed-loop system, basis pursuit de-noising, system identification, time-delay estimation.

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“…The following step is to estimate the feedback channel parameters. According to the model equivalence principle [25], and from (2), we haveŜ =ηθ c , wherê…”

Section: Identification Algorithmmentioning

confidence: 99%

Section: Simulation Examplementioning

confidence: 99%

See 1 more Smart Citation

A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

Chen¹,

Liu

Chen

et al. 2020

IEEE Access

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“…In [18] exponential smoothing is used-the Box-Jenkins method. The Box-Jenkins model [17] is based on the processing of autoregressive series in ISSN 2226-3780…”

Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

Forecasting of the performance of the shipboard electric power system on the basis of the artificial neural network

Гвоздева

Lukovtsev

Tierielnyk

2017

TAPR

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“…In recent years, SVM and its modification LSSVM have gained great attention in the field of machine learning as a method for classification and nonlinear function estimation [21][22][23][24][25][26]. This is because of its better generalization ability and global optimization property over other machine learning methods, such as PLS and ANN.…”

Section: Introductionmentioning

confidence: 99%

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

Zhang

Liu

2016

Journal of Chemometrics

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The accurate and reliable real‐time prediction of melt index (MI) is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a real‐time soft sensor based on optimized least squares support vector machine (LSSVM) for MI prediction. First, the hybrid continuous ant colony differential evolution algorithm (HACDE) is proposed to optimize the parameters of LSSVM. Then, considering the complexity and nondeterminacy of PP plant, an online correcting strategy (OCS) is adopted to update the modeling data and to revise the model's parameters adaptively. Thus, the real‐time prediction model, HACDE‐OCS‐LSSVM, is obtained. Based on the data from a real PP plant, the models of HACDE‐LSSVM, DE‐LSSVM and LSSVM are also developed for comparison. The research results show that the proposed real‐time model achieves a good performance in the practical industrial MI prediction process. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

The model equivalence based parameter estimation methods for Box–Jenkins systems

Cited by 14 publications

References 40 publications

A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

Forecasting of the performance of the shipboard electric power system on the basis of the artificial neural network

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

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