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dc.contributor.authorKarlık, Bekir
dc.contributor.authorYüksek, Kemal
dc.date.accessioned2018-07-27T14:33:57Z
dc.date.available2018-07-27T14:33:57Z
dc.date.issued2007
dc.identifier.issn1463-9246
dc.identifier.other1464-5068
dc.identifier.urihttps://doi.org/10.1155/2007/38405
dc.identifier.urihttps://hdl.handle.net/11413/2391
dc.description.abstractThe aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network. Copyright (c) 2007tr_TR
dc.language.isoen_UStr_TR
dc.publisherHindawi Publishing Corp, 315 Madison Ave 3Rd Flr, Ste 3070, New York, Ny 10017 USAtr_TR
dc.relationJournal of Automated Methods & Management in Chemistrytr_TR
dc.subjectElectronic Nosetr_TR
dc.subjectArchitecturetr_TR
dc.subjectRecordertr_TR
dc.titleFuzzy clustering neural networks for real-time odor recognition systemtr_TR
dc.typeArticletr_TR
dc.contributor.authorID3465tr_TR
dc.contributor.authorID141282tr_TR
dc.identifier.wos252051800001
dc.identifier.wos252051800001en
dc.identifier.scopus2-s2.0-36949004128
dc.identifier.scopus2-s2.0-36949004128en


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