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dc.contributor.authorUcan, ON
dc.contributor.authorBilgili, E
dc.contributor.authorGoknar, IC
dc.date.accessioned2021-03-02T21:11:18Z
dc.date.available2021-03-02T21:11:18Z
dc.date.issued2005
dc.identifier.citationBilgili E., Goknar I., Ucan O., "Cellular neural network with trapezoidal activation function", INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, cilt.33, sa.5, ss.393-417, 2005
dc.identifier.issn0098-9886
dc.identifier.othervv_1032021
dc.identifier.otherav_05aa598d-2d34-4a51-bf24-f529645620bb
dc.identifier.urihttp://hdl.handle.net/20.500.12627/9687
dc.identifier.urihttps://doi.org/10.1002/cta.328
dc.description.abstractThis paper presents a cellular neural network (CNN) scheme employing a new non-linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non-separable data points and realize Boolean operations (including eXclusive OR) by using only a single-layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright (c) 2005 John Wiley & Sons, Ltd.
dc.language.isoeng
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectMühendislik
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectSinyal İşleme
dc.subjectMühendislik ve Teknoloji
dc.titleCellular neural network with trapezoidal activation function
dc.typeMakale
dc.relation.journalINTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
dc.contributor.department, ,
dc.identifier.volume33
dc.identifier.issue5
dc.identifier.startpage393
dc.identifier.endpage417
dc.contributor.firstauthorID176179


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