An enhancement of constraint feasibility in BPN based approximate optimization
DC Field | Value | Language |
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dc.contributor.author | Lee, Jongsoo | - |
dc.contributor.author | Jeong, Heeseok | - |
dc.contributor.author | Choi, Dong-Hoon | - |
dc.contributor.author | Volovoi, Vitali | - |
dc.contributor.author | Mavris, Dimitri | - |
dc.date.accessioned | 2022-12-21T08:58:10Z | - |
dc.date.available | 2022-12-21T08:58:10Z | - |
dc.date.created | 2022-08-26 | - |
dc.date.issued | 2007-03 | - |
dc.identifier.issn | 0045-7825 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180364 | - |
dc.description.abstract | Back-propagation neural networks (BPN) have been extensively used as global approximation tools in the context of approximate optimization. A traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs. When BPN is used as a meta-model for inequality constraint function, approximate optimal solutions are sometimes actually infeasible in a case where they are active at the constraint boundary. The paper explores the development of the efficient BPN based meta-model that enhances the constraint feasibility of approximate optimal solution. The BPN based meta-model is optimized via exterior penalty method to optimally determine interconnection weights between layers in the network. The proposed approach is verified through a simple mathematical function and a ten-bar planar truss problem. For constrained approximate optimization, design of rotor blade is conducted to support the proposed strategies. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE SA | - |
dc.title | An enhancement of constraint feasibility in BPN based approximate optimization | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Dong-Hoon | - |
dc.identifier.doi | 10.1016/j.cma.2006.11.005 | - |
dc.identifier.scopusid | 2-s2.0-33846901619 | - |
dc.identifier.wosid | 000244996500006 | - |
dc.identifier.bibliographicCitation | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, v.196, no.17-20, pp.2147 - 2160 | - |
dc.relation.isPartOf | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING | - |
dc.citation.title | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING | - |
dc.citation.volume | 196 | - |
dc.citation.number | 17-20 | - |
dc.citation.startPage | 2147 | - |
dc.citation.endPage | 2160 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Mechanics | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mechanics | - |
dc.subject.keywordAuthor | back-propagation neural networks | - |
dc.subject.keywordAuthor | inequality constraints | - |
dc.subject.keywordAuthor | constrained approximate optimization | - |
dc.subject.keywordAuthor | genetic algorithm | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0045782506003690?via%3Dihub | - |
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