Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Saravanakumar, Ramasamy | - |
dc.contributor.author | Kang, Hyung Soo | - |
dc.contributor.author | Alm, Choon Ki | - |
dc.contributor.author | Su, Xiaojie | - |
dc.contributor.author | Karimi, Hamid Reza | - |
dc.date.accessioned | 2021-09-01T18:11:09Z | - |
dc.date.available | 2021-09-01T18:11:09Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-03 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/67168 | - |
dc.description.abstract | This paper examines the robust stabilization problem of continuous-time delayed neural networks via the dissipativity-learning approach. A new learning algorithm is established to guarantee the asymptotic stability as well as the (Q, S, R)-alpha-dissipativity of the considered neural networks. The developed result encompasses some existing results, such as H-infinity and passivity performances, in a unified framework. With the introduction of a Lyapunov-Krasovskii functional together with the Legendre polynomial, a novel delay-dependent linear matrix inequality (LMI) condition and a learning algorithm for robust stabilization are presented. Demonstrative examples are given to show the usefulness of the established learning algorithm. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | NONLINEAR-SYSTEMS | - |
dc.subject | DISCRETE | - |
dc.title | Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Alm, Choon Ki | - |
dc.identifier.doi | 10.1109/TNNLS.2018.2852807 | - |
dc.identifier.scopusid | 2-s2.0-85050997468 | - |
dc.identifier.wosid | 000459536100022 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.30, no.3, pp.913 - 922 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.volume | 30 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 913 | - |
dc.citation.endPage | 922 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | NONLINEAR-SYSTEMS | - |
dc.subject.keywordPlus | DISCRETE | - |
dc.subject.keywordAuthor | Dissipativity learning | - |
dc.subject.keywordAuthor | Legendre polynomial | - |
dc.subject.keywordAuthor | neural networks | - |
dc.subject.keywordAuthor | robust stabilization | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.