TRAINING THE MULTIFEEDBACK-LAYER NEURAL NETWORK USING THE PARTICLE SWARM OPTIMIZATION ALGORITHM

dc.authoridAksu, Inayet Ozge/0000-0002-0963-2982
dc.contributor.authorAksu, Inayet Ozge
dc.contributor.authorCoban, Ramazan
dc.date.accessioned2025-01-06T17:43:45Z
dc.date.available2025-01-06T17:43:45Z
dc.date.issued2013
dc.description10th International Conference on Electronics, Computer and Computation (ICECCO) -- NOV 07-09, 2013 -- Turgut Ozal Univ, Ankara, TURKEY
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.
dc.description.sponsorshipInst Elect & Elect Engineers
dc.identifier.endpage175
dc.identifier.isbn978-1-4799-3343-3
dc.identifier.startpage172
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2767
dc.identifier.wosWOS:000336616500044
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2013 International Conference on Electronics, Computer and Computation (Icecco)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectMultifeedback-Layer Neural Network
dc.subjectrecurrent neural networks
dc.subjectParticle Swarm Optimization
dc.subjectdynamic system identification
dc.subjecttraining procedure
dc.titleTRAINING THE MULTIFEEDBACK-LAYER NEURAL NETWORK USING THE PARTICLE SWARM OPTIMIZATION ALGORITHM
dc.typeConference Object

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