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Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion

Yunong Zhang, Yihong Ling, Shuai Li Orcid Logo, Min Yang, Ning Tan

Neurocomputing, Volume: 386, Pages: 126 - 135

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.neucom.2019.12.053

Abstract

Time-varying linear matrix equations and inequations have been widely studied in recent years. Time-varying Sylvester-transpose matrix inequation, which is an important variant, has not been fully investigated. Solving the time-varying problem in a constructive manner remains a challenge. This study...

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Published in: Neurocomputing
ISSN: 0925-2312
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53117
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spelling 2021-03-18T15:06:59.2404616 v2 53117 2020-01-06 Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-01-06 MECH Time-varying linear matrix equations and inequations have been widely studied in recent years. Time-varying Sylvester-transpose matrix inequation, which is an important variant, has not been fully investigated. Solving the time-varying problem in a constructive manner remains a challenge. This study considers an exp-aided conversion from time-varying linear matrix inequations to equations to solve the intractable problem. On the basis of zeroing neural network (ZNN) method, a continuous-time zeroing neural network (CTZNN) model is derived with the help of Kronecker product and vectorization technique. The convergence property of the model is analyzed. Two discrete-time ZNN models are obtained with the theoretical analyses of truncation error by using two Zhang et al.’s discretization (ZeaD) formulas with different precision to discretize the CTZNN model. The comparative numerical experiments are conducted for two discrete-time ZNN models, and the corresponding numerical results substantiate the convergence and effectiveness of two ZNN discrete-time models. Journal Article Neurocomputing 386 126 135 Elsevier BV 0925-2312 Zeroing neural network, Time-varying Sylvester-transpose matrix inequation, ZeaD formula, Discrete-time model, Exp-aided conversion 1 4 2020 2020-04-01 10.1016/j.neucom.2019.12.053 http://dx.doi.org/10.1016/j.neucom.2019.12.053 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-03-18T15:06:59.2404616 2020-01-06T15:36:27.8799808 Professional Services ISS - Uncategorised Yunong Zhang 1 Yihong Ling 2 Shuai Li 0000-0001-8316-5289 3 Min Yang 4 Ning Tan 5 53117__16447__0de11ad97a5546349da4f372999c6766.pdf 53117.pdf 2020-01-27T11:17:40.0722783 Output 2315424 application/pdf Accepted Manuscript true 2020-12-18T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion
spellingShingle Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion
Shuai Li
title_short Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion
title_full Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion
title_fullStr Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion
title_full_unstemmed Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion
title_sort Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Yunong Zhang
Yihong Ling
Shuai Li
Min Yang
Ning Tan
format Journal article
container_title Neurocomputing
container_volume 386
container_start_page 126
publishDate 2020
institution Swansea University
issn 0925-2312
doi_str_mv 10.1016/j.neucom.2019.12.053
publisher Elsevier BV
college_str Professional Services
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hierarchy_top_id professionalservices
hierarchy_top_title Professional Services
hierarchy_parent_id professionalservices
hierarchy_parent_title Professional Services
department_str ISS - Uncategorised{{{_:::_}}}Professional Services{{{_:::_}}}ISS - Uncategorised
url http://dx.doi.org/10.1016/j.neucom.2019.12.053
document_store_str 1
active_str 0
description Time-varying linear matrix equations and inequations have been widely studied in recent years. Time-varying Sylvester-transpose matrix inequation, which is an important variant, has not been fully investigated. Solving the time-varying problem in a constructive manner remains a challenge. This study considers an exp-aided conversion from time-varying linear matrix inequations to equations to solve the intractable problem. On the basis of zeroing neural network (ZNN) method, a continuous-time zeroing neural network (CTZNN) model is derived with the help of Kronecker product and vectorization technique. The convergence property of the model is analyzed. Two discrete-time ZNN models are obtained with the theoretical analyses of truncation error by using two Zhang et al.’s discretization (ZeaD) formulas with different precision to discretize the CTZNN model. The comparative numerical experiments are conducted for two discrete-time ZNN models, and the corresponding numerical results substantiate the convergence and effectiveness of two ZNN discrete-time models.
published_date 2020-04-01T04:05:56Z
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score 11.036706

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