Solving maximum clique problem by cellular neural network

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dc.citation.epage1506en_US
dc.citation.issueNumber15en_US
dc.citation.spage1504en_US
dc.citation.volumeNumber34en_US
dc.contributor.authorŞengör, N. S.en_US
dc.contributor.authorYalçın, M. E.en_US
dc.contributor.authorÇakır, Y.en_US
dc.contributor.authorÜçer, M.en_US
dc.contributor.authorGüzeliş, C.en_US
dc.contributor.authorPekergin, F.en_US
dc.contributor.authorMorgül, Ö.en_US
dc.date.accessioned2016-02-08T10:44:36Z
dc.date.available2016-02-08T10:44:36Z
dc.date.issued1998-07-23en_US
dc.description.abstractAn approximate solution of an NP-hard graph theoretical problem, namely finding maximum clique, is presented using cellular neural networks. Like the existing energy descent optimising dynamics, the maximal cliques will be the stable states of cellular neural networks. To illustrate the performance of the method, the results will be compared with those of continuous Hopfield dynamics.en_US
dc.identifier.doi10.1049/el:19981026en_US
dc.identifier.issn0013-5194
dc.identifier.urihttp://hdl.handle.net/11693/25434
dc.language.isoEnglishen_US
dc.publisherThe Institution of Engineering and Technologyen_US
dc.relation.isversionofhttps://doi.org/10.1049/el:19981026en_US
dc.source.titleElectronics Lettersen_US
dc.subjectApproximation theoryen_US
dc.subjectComputational complexityen_US
dc.subjectGraph theoryen_US
dc.subjectHopfield dynamicsen_US
dc.subjectMaximum clique problemen_US
dc.subjectCellular neural networksen_US
dc.titleSolving maximum clique problem by cellular neural networken_US
dc.typeArticleen_US
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