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dc.contributor.authorSu, CTen_US
dc.contributor.authorChang, HHen_US
dc.date.accessioned2014-12-08T15:44:34Z-
dc.date.available2014-12-08T15:44:34Z-
dc.date.issued2000-12-01en_US
dc.identifier.issn0020-7721en_US
dc.identifier.urihttp://dx.doi.org/10.1080/00207720050217313en_US
dc.identifier.urihttp://hdl.handle.net/11536/30088-
dc.description.abstractParameter design optimization problems have found extensive industrial applications, including product development, process design and operational condition setting. The parameter design optimization problems are complex because non-lineal relationships and interactions may occur among parameters. To resolve such problems, engineers commonly employ the Taguchi method. However, the Taguchi method has some limitations in practice. Therefore, in this work, we present a novel means of improving the effectiveness of the optimization of parameter design. The proposed approach employs the neural network and simulated annealing, and consists of two phases. Phase I formulates an objective function for a problem using a neural network method to predict the value of the response for a given parameter setting. Phase 2 applies the simulated annealing algorithm to search for the optimal parameter combination. A numerical example demonstrates the effectiveness of the proposed approach.en_US
dc.language.isoen_USen_US
dc.titleOptimization of parameter design: an intelligent approach using neural network and simulated annealingen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00207720050217313en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF SYSTEMS SCIENCEen_US
dc.citation.volume31en_US
dc.citation.issue12en_US
dc.citation.spage1543en_US
dc.citation.epage1549en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000166013000003-
dc.citation.woscount21-
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