Training the multifeedback-layer neural network using the Particle Swarm Optimization algorithm

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dc.contributor.authorAksu, Inayet Ozge
dc.contributor.authorCoban, Ramazan
dc.date.accessioned2025-01-06T17:29:42Z
dc.date.available2025-01-06T17:29:42Z
dc.date.issued2013
dc.description2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 -- 7 November 2013 through 8 November 2013 -- Ankara -- 102696
dc.description.abstractIn this study, the Multifeedback-Layer Neural Network (MFLNN) weights are trained by the Particle Swarm Optimization (PSO). This method (MFLNN-PSO) is applied to two different problems to prove accomplishment of the study. Firstly, a chaotic time series prediction problem is used to test the MFLNN-PSO. Also, the method is used for identification of a non-linear dynamic system. This study shows that the MFLNN-PSO can be used for dynamic system identification as well as controller design. © 2013 IEEE.
dc.identifier.doi10.1109/ICECCO.2013.6718256
dc.identifier.endpage175
dc.identifier.isbn978-147993343-3
dc.identifier.scopus2-s2.0-84894146124
dc.identifier.startpage172
dc.identifier.urihttps://doi.org/10.1109/ICECCO.2013.6718256
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1298
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.ispartof2013 International Conference on Electronics, Computer and Computation, ICECCO 2013
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectdynamic system identification
dc.subjectMultifeedback-Layer Neural Network
dc.subjectParticle Swarm Optimization
dc.subjectrecurrent neural networks
dc.subjecttraining procedure
dc.titleTraining the multifeedback-layer neural network using the Particle Swarm Optimization algorithm
dc.typeConference Object

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