Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm

Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm

The parameter estimation problem of the ARX model is studied in this paper. First, some traditional identification algorithms are briefly introduced, and then a new parameter estimation algorithm—the modified momentum gradient descent algorithm—is developed. Two gradient directions with their corres...

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Main Authors: Quan Tu, Yingjiao Rong, Jing Chen
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/9537075
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spelling doaj-efdb3a40da954fa9986b8573396852302020-11-25T03:23:45ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/95370759537075Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent AlgorithmQuan Tu0Yingjiao Rong1Jing Chen2School of Science, Jiangnan University, Wuxi 214122, ChinaThe Science and Technology on Near-Surface Detection Laboratory, Wuxi 214028, ChinaSchool of Science, Jiangnan University, Wuxi 214122, ChinaThe parameter estimation problem of the ARX model is studied in this paper. First, some traditional identification algorithms are briefly introduced, and then a new parameter estimation algorithm—the modified momentum gradient descent algorithm—is developed. Two gradient directions with their corresponding step sizes are derived in each iteration. Compared with the traditional parameter identification algorithms, the modified momentum gradient descent algorithm has a faster convergence rate. A simulation example shows that the proposed algorithm is effective.http://dx.doi.org/10.1155/2020/9537075
collection DOAJ
language English
format Article
sources DOAJ
author Quan Tu
Yingjiao Rong
Jing Chen
spellingShingle Quan Tu
Yingjiao Rong
Jing Chen
Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm
Complexity
author_facet Quan Tu
Yingjiao Rong
Jing Chen
author_sort Quan Tu
title Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm
title_short Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm
title_full Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm
title_fullStr Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm
title_full_unstemmed Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm
title_sort parameter identification of arx models based on modified momentum gradient descent algorithm
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description The parameter estimation problem of the ARX model is studied in this paper. First, some traditional identification algorithms are briefly introduced, and then a new parameter estimation algorithm—the modified momentum gradient descent algorithm—is developed. Two gradient directions with their corresponding step sizes are derived in each iteration. Compared with the traditional parameter identification algorithms, the modified momentum gradient descent algorithm has a faster convergence rate. A simulation example shows that the proposed algorithm is effective.
url http://dx.doi.org/10.1155/2020/9537075
work_keys_str_mv AT quantu parameteridentificationofarxmodelsbasedonmodifiedmomentumgradientdescentalgorithm
AT yingjiaorong parameteridentificationofarxmodelsbasedonmodifiedmomentumgradientdescentalgorithm
AT jingchen parameteridentificationofarxmodelsbasedonmodifiedmomentumgradientdescentalgorithm
_version_ 1715228147381698560

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