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dc.contributor.authorJan, Jiun-Chien_US
dc.contributor.authorHung, Shih-Linen_US
dc.date.accessioned2014-12-08T15:12:25Z-
dc.date.available2014-12-08T15:12:25Z-
dc.date.issued2008-04-01en_US
dc.identifier.issn1370-4621en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s11063-007-9067-4en_US
dc.identifier.urihttp://hdl.handle.net/11536/9537-
dc.description.abstractMacro_Structure_CMAC (MS_CMAC) is a variational CMAC neural network that is designed for modeling smooth functional mappings. The MS_CMAC learning strategy involves constructing virtual grid-distributed data points from random-distributed training data points, and then using the virtual data points to train a tree structure network that is composed of one-dimensional CMAC nodes. A disadvantage of the MS_CMAC is that the prediction errors near the boundary area might sometimes be unexpectedly large. Another disadvantage of the MS_CMAC is that generating virtual grid-distributed data points generally takes a long computational time. Therefore, this study develops an improved model by integrating an unsupervised fuzzy neural network (UFN) into the MS_CMAC to initialize systematically the virtual grid-distributed data points. Additionally, a new error feedback ratio function is adopted to speed up the MS_CMAC training. Several numerical problems are considered to test the improved MS_CMAC. The computed results indicate that a simplified UFN model can produce good initial values of the virtual grid-distributed data points to aggrandize MS_CMAC training. The MS_CMAC prediction is also improved by using the initialized virtual grid-distributed data points.en_US
dc.language.isoen_USen_US
dc.subjectvirtual grid-distributed data pointsen_US
dc.subjecta tree structure networken_US
dc.subjectUFNen_US
dc.titleImproved MS_CMAC neural networks by integrating a simplified UFN modelen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s11063-007-9067-4en_US
dc.identifier.journalNEURAL PROCESSING LETTERSen_US
dc.citation.volume27en_US
dc.citation.issue2en_US
dc.citation.spage163en_US
dc.citation.epage177en_US
dc.contributor.department土木工程學系zh_TW
dc.contributor.departmentDepartment of Civil Engineeringen_US
dc.identifier.wosnumberWOS:000253529100006-
dc.citation.woscount0-
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