Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Choon Ki | - |
dc.contributor.author | Lim, Myo Taeg | - |
dc.date.accessioned | 2021-09-05T18:32:17Z | - |
dc.date.available | 2021-09-05T18:32:17Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2013-12 | - |
dc.identifier.issn | 0941-0643 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/101509 | - |
dc.description.abstract | This paper investigates the model predictive stabilization problemfor Takagi-Sugeno (T-S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T-S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER LONDON LTD | - |
dc.subject | FEEDBACK STABILIZATION | - |
dc.subject | STATE ESTIMATION | - |
dc.subject | SYSTEMS | - |
dc.title | Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.contributor.affiliatedAuthor | Lim, Myo Taeg | - |
dc.identifier.doi | 10.1007/s00521-013-1381-3 | - |
dc.identifier.scopusid | 2-s2.0-84888819586 | - |
dc.identifier.wosid | 000330030100022 | - |
dc.identifier.bibliographicCitation | NEURAL COMPUTING & APPLICATIONS, v.23, pp.S271 - S277 | - |
dc.relation.isPartOf | NEURAL COMPUTING & APPLICATIONS | - |
dc.citation.title | NEURAL COMPUTING & APPLICATIONS | - |
dc.citation.volume | 23 | - |
dc.citation.startPage | S271 | - |
dc.citation.endPage | S277 | - |
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, Artificial Intelligence | - |
dc.subject.keywordPlus | FEEDBACK STABILIZATION | - |
dc.subject.keywordPlus | STATE ESTIMATION | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordAuthor | Model predictive stabilization | - |
dc.subject.keywordAuthor | Takagi-Sugeno (T-S) fuzzy neural networks | - |
dc.subject.keywordAuthor | Cost monotonicity | - |
dc.subject.keywordAuthor | Linear matrix inequality (LMI) | - |
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