Bayesian Estimation of Discrete-Time Cellular Neural Network Coefficients

dc.contributor.author Özer, Hakan Metin
dc.contributor.author Şenol, Habib
dc.contributor.author Özmen, Atilla
dc.contributor.author Özmen, Atilla
dc.contributor.author Şenol, Habib
dc.contributor.other Computer Engineering
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2019-06-27T08:01:32Z
dc.date.available 2019-06-27T08:01:32Z
dc.date.issued 2017
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü en_US
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü en_US
dc.description.abstract A 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. en_US]
dc.identifier.citationcount 1
dc.identifier.doi 10.3906/elk-1510-87 en_US
dc.identifier.endpage 2374
dc.identifier.issn 1300-0632 en_US
dc.identifier.issn 1303-6203 en_US
dc.identifier.issn 1300-0632
dc.identifier.issn 1303-6203
dc.identifier.issue 3
dc.identifier.scopus 2-s2.0-85020735615 en_US
dc.identifier.scopusquality Q3
dc.identifier.startpage 2363 en_US
dc.identifier.trdizinid 247754 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/397
dc.identifier.uri https://doi.org/10.3906/elk-1510-87
dc.identifier.uri https://search.trdizin.gov.tr/yayin/detay/247754
dc.identifier.volume 25 en_US
dc.identifier.wos WOS:000404385700059 en_US
dc.identifier.wosquality Q4
dc.institutionauthor Şenol, Habib en_US
dc.language.iso en en_US
dc.publisher TUBITAK Scientific & Technical Research Council Turkey en_US
dc.relation.journal Turkish Journal of Electrical Engineering & Computer Sciences en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 1
dc.subject Bayesian learning en_US
dc.subject Cellular neural networks en_US
dc.subject Metropolis Hastings en_US
dc.subject Estimation en_US
dc.title Bayesian Estimation of Discrete-Time Cellular Neural Network Coefficients en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication
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