Recognizing handwritten digits using spiking neural networks with learning algorithms based on sliding mode control theory

dc.WoS.categoriesComputer Science, Artificial IntelligenceEngineering, Electrical & Electronicen_US
dc.authorid0000-0003-3411-6215en_US
dc.contributor.authorÖniz, Yeşim
dc.contributor.authorAyyıldız, Mehmet
dc.date.accessioned2024-04-03T07:13:08Z
dc.date.available2024-04-03T07:13:08Z
dc.date.issued2023-10-26
dc.description.abstractIn this paper, a spiking neural network (SNN) has been proposed for recognizing the digits written on the LCD screen of an experimental setup. The convergence of the learning algorithm has been ensured by using sliding mode control (SMC) theory and the Lyapunov stability method for the adaptation of the network parameters. The spike response model (SRM) has been utilized in the design of the SNN. The performance of the proposed learning scheme has been evaluated both on the experimental data and on the MNIST dataset. The simulated and experimental results of the SNN structure have been compared with the responses of a conventional neural network (ANN) for which the weight update rules have been also derived using SMC theory. The conducted simulations and experimental studies reveal that convergence can be ensured for the proposed learning scheme and the SNN yields higher recognition accuracy compared to a conventional ANN.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.55730/1300-0632.4022en_US
dc.identifier.issn1303-6203
dc.identifier.issn1300-0632
dc.identifier.scopus2-s2.0-85174575548en_US
dc.identifier.trdizinid1208552en_US
dc.identifier.urihttps://hdl.handle.net/11411/5236
dc.identifier.urihttps://doi.org/10.55730/1300-0632.4022
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1208552en_US
dc.identifier.wosWOS:001080270600007en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.issue5en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors2en_US
dc.pages860-875en_US
dc.publisherScientific and Technological Research Council Turkeyen_US
dc.relation.ecYesen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDigit recognitionen_US
dc.subjectspiking neural networksen_US
dc.subjectsliding mode controlen_US
dc.titleRecognizing handwritten digits using spiking neural networks with learning algorithms based on sliding mode control theoryen_US
dc.typeArticleen_US
dc.volume31en_US

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