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Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation

Yang Shi Orcid Logo, Long Jin Orcid Logo, Shuai Li Orcid Logo, Jipeng Qiang

Journal of the Franklin Institute, Volume: 357, Issue: 6, Pages: 3636 - 3655

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.jfranklin.2020.02.024

Abstract

In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advanc...

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Published in: Journal of the Franklin Institute
ISSN: 0016-0032
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53642
first_indexed 2020-02-27T13:41:10Z
last_indexed 2023-03-18T04:07:14Z
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spelling 2023-03-17T11:26:07.5519234 v2 53642 2020-02-27 Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-02-27 ACEM In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advance zeroing neural network (CT-AZNN) model is shown for solving continuous-time augmented Sylvester matrix equation (CT-ASME). Secondly, a recently published discretization formula is further investigated with the optimal sampling gap of the discretization formula proposed. Then, for solving F-ASME, a novel DT-AZNN model is proposed based on the discretization formula. Theoretical analyses on the convergence property and the perturbation suppression performance of the DT-AZNN model are provided. Moreover, comparative numerical experimental results are conducted to prove the effectiveness and robustness of the proposed DT-AZNN model for solving F-ASME. Journal Article Journal of the Franklin Institute 357 6 3636 3655 Elsevier BV 0016-0032 Future augmented Sylvester matrix equation, Zeroing neural network, Discretization formula, Robustness 26 4 2020 2020-04-26 10.1016/j.jfranklin.2020.02.024 http://dx.doi.org/10.1016/j.jfranklin.2020.02.024 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2023-03-17T11:26:07.5519234 2020-02-27T09:17:57.3712293 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Yang Shi 0000-0003-3014-7858 1 Long Jin 0000-0002-5329-5098 2 Shuai Li 0000-0001-8316-5289 3 Jipeng Qiang 4 53642__16706__e18334fa932e4ebba741517f0f89b000.pdf shi2020.pdf 2020-02-27T09:20:01.9755240 Output 2084451 application/pdf Accepted Manuscript true 2021-02-26T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
spellingShingle Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
Shuai Li
title_short Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
title_full Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
title_fullStr Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
title_full_unstemmed Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
title_sort Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Yang Shi
Long Jin
Shuai Li
Jipeng Qiang
format Journal article
container_title Journal of the Franklin Institute
container_volume 357
container_issue 6
container_start_page 3636
publishDate 2020
institution Swansea University
issn 0016-0032
doi_str_mv 10.1016/j.jfranklin.2020.02.024
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
url http://dx.doi.org/10.1016/j.jfranklin.2020.02.024
document_store_str 1
active_str 0
description In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advance zeroing neural network (CT-AZNN) model is shown for solving continuous-time augmented Sylvester matrix equation (CT-ASME). Secondly, a recently published discretization formula is further investigated with the optimal sampling gap of the discretization formula proposed. Then, for solving F-ASME, a novel DT-AZNN model is proposed based on the discretization formula. Theoretical analyses on the convergence property and the perturbation suppression performance of the DT-AZNN model are provided. Moreover, comparative numerical experimental results are conducted to prove the effectiveness and robustness of the proposed DT-AZNN model for solving F-ASME.
published_date 2020-04-26T13:51:43Z
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score 11.048149

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