Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Choon Ki | - |
dc.date.accessioned | 2021-09-05T04:18:38Z | - |
dc.date.available | 2021-09-05T04:18:38Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2014-10-01 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/97128 | - |
dc.description.abstract | In this paper, we propose a new receding horizon disturbance attenuator (RHDA) for Takagi-Sugeno (T-S) fuzzy switched Hopfield neural networks with external disturbance. First, a new set of linear matrix inequality (LMI) conditions is proposed for the finite terminal weighting matrix of the receding horizon cost function with a cross term. Second, under this condition, we show that the proposed RHDA attenuates the effect of external disturbance on T-S fuzzy switched Hopfield neural networks with a guaranteed infinite horizon H-infinity performance. In addition, we prove that the proposed RHDA guarantees internal stability in closed-loop systems. A numerical example is presented to describe the effectiveness of the proposed RHDA scheme. (C) 2014 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | STABILITY ANALYSIS | - |
dc.subject | ROBUST STABILITY | - |
dc.subject | FEEDBACK STABILIZATION | - |
dc.subject | STATE ESTIMATION | - |
dc.subject | FILTER DESIGN | - |
dc.subject | SYSTEMS | - |
dc.subject | DELAY | - |
dc.subject | CRITERIA | - |
dc.title | Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.identifier.doi | 10.1016/j.ins.2014.04.024 | - |
dc.identifier.scopusid | 2-s2.0-84902528860 | - |
dc.identifier.wosid | 000339132700004 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.280, pp.53 - 63 | - |
dc.relation.isPartOf | INFORMATION SCIENCES | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 280 | - |
dc.citation.startPage | 53 | - |
dc.citation.endPage | 63 | - |
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.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | STABILITY ANALYSIS | - |
dc.subject.keywordPlus | ROBUST STABILITY | - |
dc.subject.keywordPlus | FEEDBACK STABILIZATION | - |
dc.subject.keywordPlus | STATE ESTIMATION | - |
dc.subject.keywordPlus | FILTER DESIGN | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | DELAY | - |
dc.subject.keywordPlus | CRITERIA | - |
dc.subject.keywordAuthor | Neuro-fuzzy system | - |
dc.subject.keywordAuthor | Fuzzy system model | - |
dc.subject.keywordAuthor | Switched neural network | - |
dc.subject.keywordAuthor | Receding horizon disturbance attenuator (RHDA) | - |
dc.subject.keywordAuthor | Linear matrix inequality (LMI) | - |
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