Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach

<p dir="ltr">This note introduces a unified matrix-measure concept to study the stability of a class of inertial neural networks with bounded time delays on time scales. The novel matrix-measure concept unifies the classic matrix-measure and the generalized matrix-measure concept. On...

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Main Author: Qiang Xiao (447702) (author)
Other Authors: Tingwen Huang (7168691) (author)
Published: 2020
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author Qiang Xiao (447702)
author2 Tingwen Huang (7168691)
author2_role author
author_facet Qiang Xiao (447702)
Tingwen Huang (7168691)
author_role author
dc.creator.none.fl_str_mv Qiang Xiao (447702)
Tingwen Huang (7168691)
dc.date.none.fl_str_mv 2020-10-01T00:00:00Z
dc.identifier.none.fl_str_mv 10.1016/j.neunet.2020.06.020
dc.relation.none.fl_str_mv https://figshare.com/articles/journal_contribution/Stability_of_delayed_inertial_neural_networks_on_time_scales_A_unified_matrix-measure_approach/24270313
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Information and computing sciences
Artificial intelligence
Data management and data science
Mathematical sciences
Pure mathematics
Stability
Inertial neural network
Time scale
Unified matrix-measure
Time delay
dc.title.none.fl_str_mv Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
dc.type.none.fl_str_mv Text
Journal contribution
info:eu-repo/semantics/publishedVersion
text
contribution to journal
description <p dir="ltr">This note introduces a unified matrix-measure concept to study the stability of a class of inertial neural networks with bounded time delays on time scales. The novel matrix-measure concept unifies the classic matrix-measure and the generalized matrix-measure concept. One sufficient global exponential stability criterion is obtained based on this key matrix-measure and no Lyapunov function is required. To make the stability performance better, another stability criterion in which more detailed information is involved has been acquired. The theoretical results in this note contain and extend some existing continuous-time and discrete-time works. A numerical example is given to show the validity of the results.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Networks<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.neunet.2020.06.020" target="_blank">https://dx.doi.org/10.1016/j.neunet.2020.06.020</a></p>
eu_rights_str_mv openAccess
id Manara2_9b74ff6038b29f7ee65513a96cfca975
identifier_str_mv 10.1016/j.neunet.2020.06.020
network_acronym_str Manara2
network_name_str Manara2
oai_identifier_str oai:figshare.com:article/24270313
publishDate 2020
repository.mail.fl_str_mv
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rights_invalid_str_mv CC BY 4.0
spelling Stability of delayed inertial neural networks on time scales: A unified matrix-measure approachQiang Xiao (447702)Tingwen Huang (7168691)Information and computing sciencesArtificial intelligenceData management and data scienceMathematical sciencesPure mathematicsStabilityInertial neural networkTime scaleUnified matrix-measureTime delay<p dir="ltr">This note introduces a unified matrix-measure concept to study the stability of a class of inertial neural networks with bounded time delays on time scales. The novel matrix-measure concept unifies the classic matrix-measure and the generalized matrix-measure concept. One sufficient global exponential stability criterion is obtained based on this key matrix-measure and no Lyapunov function is required. To make the stability performance better, another stability criterion in which more detailed information is involved has been acquired. The theoretical results in this note contain and extend some existing continuous-time and discrete-time works. A numerical example is given to show the validity of the results.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Networks<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.neunet.2020.06.020" target="_blank">https://dx.doi.org/10.1016/j.neunet.2020.06.020</a></p>2020-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.neunet.2020.06.020https://figshare.com/articles/journal_contribution/Stability_of_delayed_inertial_neural_networks_on_time_scales_A_unified_matrix-measure_approach/24270313CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/242703132020-10-01T00:00:00Z
spellingShingle Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
Qiang Xiao (447702)
Information and computing sciences
Artificial intelligence
Data management and data science
Mathematical sciences
Pure mathematics
Stability
Inertial neural network
Time scale
Unified matrix-measure
Time delay
status_str publishedVersion
title Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
title_full Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
title_fullStr Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
title_full_unstemmed Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
title_short Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
title_sort Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach
topic Information and computing sciences
Artificial intelligence
Data management and data science
Mathematical sciences
Pure mathematics
Stability
Inertial neural network
Time scale
Unified matrix-measure
Time delay