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dc.contributor.authorHu, YCen_US
dc.contributor.authorChen, RSen_US
dc.contributor.authorHsu, YTen_US
dc.contributor.authorTzeng, GHen_US
dc.date.accessioned2014-12-08T15:41:52Z-
dc.date.available2014-12-08T15:41:52Z-
dc.date.issued2002-10-01en_US
dc.identifier.issn0925-2312en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0925-2312(01)00677-4en_US
dc.identifier.urihttp://hdl.handle.net/11536/28473-
dc.description.abstractIn each training iteration of the self-organizing feature maps (SOFM), the adjustable output nodes can be determined by the neighborhood size of the winning node. However, it seems that the SOFM ignores some important information, which is the relationships that actually exist between the input training data and each adjustable output node, in the learning rule. By viewing input data and each adjustable node as a reference sequence and a comparative sequence, respectively, the grey relations between these sequences can be seen. This paper thus incorporates the grey relational coefficient into the learning rule of the SOFM, and a grey clustering method, namely the GSOFM, is proposed. From the simulation results, we can see that the best result of the proposed method applied for analysis of the iris data outperforms those of other known unsupervised neural network models. Furthermore, the proposed method can effectively solve the traveling salesman problem. (C) 2002 Elsevier Science B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectself-organizing feature mapsen_US
dc.subjectgrey relationen_US
dc.subjectgrey clusteringen_US
dc.subjecttraveling salesman problemen_US
dc.titleGrey self-organizing feature mapsen_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.doi10.1016/S0925-2312(01)00677-4en_US
dc.identifier.journalNEUROCOMPUTINGen_US
dc.citation.volume48en_US
dc.citation.issueen_US
dc.citation.spage863en_US
dc.citation.epage877en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.department资讯管理与财务金融系
注:原资管所+财金所
zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000178464600049-
显示于类别:Conferences Paper


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