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dc.contributor.authorBecerikli, Yaşar
dc.contributor.authorKonar, Ahmet Ferit
dc.contributor.authorSamad, Tarıq
dc.date.accessioned2014-12-08T14:54:27Z
dc.date.available2014-12-08T14:54:27Z
dc.date.issued2003-03
dc.identifier.citationBECERİKLİ, Y., KONAR. A.F., SMAD, T. (2003). Intelligent optimal control with dynamic neural networks. Neural Networks, Volume 16, Issue 2, pp.251–259. http://dx.doi.org/10.1016/S0893-6080(02)00232-0en_US
dc.identifier.issn0893-6080
dc.identifier.other000182063800007 (WOS)
dc.identifier.urihttp://dx.doi.org/10.1016/S0893-6080(02)00232-0
dc.identifier.urihttp://hdl.handle.net/11376/734
dc.description.abstractThe application of neural networks technology to dynamic system control has been constrained by the non-dynamic nature of popular network architectures. Many of difficulties are-large network sizes (i.e. curse of dimensionality), long training times, etc. These problems can be overcome with dynamic neural networks (DNN). In this study, intelligent optimal control problem is considered as a nonlinear optimization with dynamic equality constraints, and DNN as a control trajectory priming system. The resulting algorithm operates as an auto-trainer for DNN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. In this way, optimal control trajectories are encapsulated and generalized by DNN. The time varying optimal feedback gains are also generated along the trajectory as byproducts. Speeding up trajectory calculations opens up avenues for real-time intelligent optimal control with virtual global feedback. We used direct-descent-curvature algorithm with some modifications (we called modified-descend-controller-MDC algorithm) for the optimal control computations. The algorithm has generated numerically very robust solutions with respect to conjugate points. The adjoint theory has been used in the training of DNN which is considered as a quasi-linear dynamic system. The updating of weights (identification of parameters) are based on Broyden-Fletcher-Goldfarb-Shanno BFGS method. Simulation results are given for an intelligent optimal control system controlling a difficult nonlinear second-order system using fully connected three-neuron DNN.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/S0893-6080(02)00232-0en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNeural Networksen_US
dc.subjectOptimal Controlen_US
dc.subjectTraining Trajectoryen_US
dc.subjectNonlinear Dynamic Optimizationen_US
dc.subjectAdjoint Theoryen_US
dc.subjectIntelligent Controlen_US
dc.titleIntelligent optimal control with dynamic neural networksen_US
dc.typearticleen_US
dc.relation.journalNeural networksen_US
dc.contributor.departmentDoğuş Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.authorIDTR2229en_US
dc.identifier.volume16en_US
dc.identifier.issue2en_US
dc.identifier.startpage251en_US
dc.identifier.endpage259en_US


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