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dc.contributor.authorOzer, Hakan Metin
dc.contributor.authorOzmen, Atilla
dc.contributor.authorŞenol, Habib
dc.date.accessioned2019-06-27T08:01:32Z
dc.date.available2019-06-27T08:01:32Z
dc.date.issued2017
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.urihttps://hdl.handle.net/20.500.12469/397
dc.identifier.urihttps://doi.org/10.3906/elk-1510-87
dc.description.abstractA new method for finding the network coefficients of a discrete-time cellular neural network (DTCNN) is proposed. This new method uses a probabilistic approach that itself uses Bayesian learning to estimate the network coefficients. A posterior probability density function (PDF) is composed using the likelihood and prior PDFs derived from the system model and prior information respectively. This posterior PDF is used to draw samples with the help of the Metropolis algorithm a special case of the Metropolis--Hastings algorithm where the proposal distribution function is symmetric and resulting samples are then averaged to find the minimum mean square error (MMSE) estimate of the network coefficients. A couple of image processing applications are performed using these estimated parameters and the results are compared with those of some well-known methods.
dc.language.isoEnglish
dc.publisherTUBITAK Scientific & Technical Research Council Turkey
dc.subjectBayesian learning
dc.subjectCellular neural networks
dc.subjectMetropolis Hastings
dc.subjectEstimation
dc.titleBayesian estimation of discrete-time cellular neural network coefficients
dc.typeArticle
dc.identifier.startpage2363
dc.identifier.endpage2374
dc.relation.journalTurkish Journal of Electrical Engineering & Computer Sciences
dc.identifier.issue3
dc.identifier.volume25
dc.identifier.wosWOS:000404385700059
dc.identifier.doi10.3906/elk-1510-87
dc.contributor.khasauthorŞenol, Habib


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