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Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator

Yang Shi Orcid Logo, Wenhan Zhao, Shuai Li Orcid Logo, Bin Li, Xiaobing Sun

Applied Soft Computing, Volume: 133, Start page: 109861

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.asoc.2022.109861

Abstract

The improvement of recurrent neural network (RNN) algorithms is one of target of many researchers, and these algorithms are wieldy used to solve time-variant problems in a variety of domains. A novel direct derivation scheme of discrete time-variant RNN (DT-RNN) algorithm for addressing discrete tim...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62067
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spelling 2022-12-28T19:26:41.9469432 v2 62067 2022-11-28 Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2022-11-28 MECH The improvement of recurrent neural network (RNN) algorithms is one of target of many researchers, and these algorithms are wieldy used to solve time-variant problems in a variety of domains. A novel direct derivation scheme of discrete time-variant RNN (DT-RNN) algorithm for addressing discrete time-variant matrix pseudo-inversion is discussed in this paper. To be more specific, firstly, a DT-RNN algorithm mathematically founded on the second-order Taylor expansion is proposed for dealing with discrete time-variant matrix pseudo-inversion, and it does not require the theoretical support of continuous time-variant RNN (CT-RNN) algorithm. Secondly, the results of theoretical analyses of the proposed DT-RNN algorithm are also presented in this paper. These results demonstrate that the novel DT-RNN algorithm has remarkable computing performance. The efficiency and applicability of the DT-RNN algorithm have been verified through one numerical experiment example and two robotic manipulator experiments. Journal Article Applied Soft Computing 133 109861 Elsevier BV 1568-4946 1 1 2023 2023-01-01 10.1016/j.asoc.2022.109861 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University This work was supported by the National Natural Science Foundation of China (with numbers 61906164 and 61972335), by the Natural Science Foundation of Jiangsu Province of China (with number BK20190875), by the Six Talent Peaks Project in Jiangsu Province (with number RJFW-053), by Jiangsu “333” Project, by Qinglan project of Yangzhou University, by High-end Talent Support Program of Yangzhou University, by the Cross-Disciplinary Project of the Animal Science Special Discipline of Yangzhou University, and by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (with numbers KYCX21_3234 and SJCX22_1709). 2022-12-28T19:26:41.9469432 2022-11-28T11:50:31.3075957 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Yang Shi 0000-0003-3014-7858 1 Wenhan Zhao 2 Shuai Li 0000-0001-8316-5289 3 Bin Li 4 Xiaobing Sun 5 Under embargo Under embargo 2022-11-28T12:03:26.5405703 Output 1888262 application/pdf Accepted Manuscript true 2023-11-25T00:00:00.0000000 ©2022 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
spellingShingle Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
Shuai Li
title_short Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
title_full Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
title_fullStr Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
title_full_unstemmed Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
title_sort Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Yang Shi
Wenhan Zhao
Shuai Li
Bin Li
Xiaobing Sun
format Journal article
container_title Applied Soft Computing
container_volume 133
container_start_page 109861
publishDate 2023
institution Swansea University
issn 1568-4946
doi_str_mv 10.1016/j.asoc.2022.109861
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 0
active_str 0
description The improvement of recurrent neural network (RNN) algorithms is one of target of many researchers, and these algorithms are wieldy used to solve time-variant problems in a variety of domains. A novel direct derivation scheme of discrete time-variant RNN (DT-RNN) algorithm for addressing discrete time-variant matrix pseudo-inversion is discussed in this paper. To be more specific, firstly, a DT-RNN algorithm mathematically founded on the second-order Taylor expansion is proposed for dealing with discrete time-variant matrix pseudo-inversion, and it does not require the theoretical support of continuous time-variant RNN (CT-RNN) algorithm. Secondly, the results of theoretical analyses of the proposed DT-RNN algorithm are also presented in this paper. These results demonstrate that the novel DT-RNN algorithm has remarkable computing performance. The efficiency and applicability of the DT-RNN algorithm have been verified through one numerical experiment example and two robotic manipulator experiments.
published_date 2023-01-01T04:21:23Z
_version_ 1763754412857098240
score 11.0022

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