Aksu, Inayet OzgeCoban, Ramazan2025-01-062025-01-062013978-147993343-310.1109/ICECCO.2013.67182562-s2.0-84894146124https://doi.org/10.1109/ICECCO.2013.6718256https://hdl.handle.net/20.500.14669/12982013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 -- 7 November 2013 through 8 November 2013 -- Ankara -- 102696In 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.eninfo:eu-repo/semantics/closedAccessdynamic system identificationMultifeedback-Layer Neural NetworkParticle Swarm Optimizationrecurrent neural networkstraining procedureConference Object175172