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dc.contributor.authorLin, CTen_US
dc.contributor.authorDuh, FBen_US
dc.contributor.authorLiu, DJen_US
dc.date.accessioned2014-12-08T15:42:37Z-
dc.date.available2014-12-08T15:42:37Z-
dc.date.issued2002-04-01en_US
dc.identifier.issn0165-0114en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0165-0114(01)00151-8en_US
dc.identifier.urihttp://hdl.handle.net/11536/28921-
dc.description.abstractA neural fuzzy system learning with fuzzy training data is proposed in this study. The system is able to process and learn numerical information as well as word information. At first, we propose a basic structure of five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Also they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only word teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs. An experimental system is constructed to illustrate the performance and applicability of the proposed scheme. (C) 2002 Elsevier Science B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.titleA neural fuzzy network for word information processingen_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.doi10.1016/S0165-0114(01)00151-8en_US
dc.identifier.journalFUZZY SETS AND SYSTEMSen_US
dc.citation.volume127en_US
dc.citation.issue1en_US
dc.citation.spage37en_US
dc.citation.epage48en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000174539100004-
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