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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2020
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| _version_ | 1864513558787653632 |
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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 | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| 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 |