Aksu, Inayet OzgeCoban, Ramazan2025-01-062025-01-062013978-1-4799-3343-3https://hdl.handle.net/20.500.14669/276710th International Conference on Electronics, Computer and Computation (ICECCO) -- NOV 07-09, 2013 -- Turgut Ozal Univ, Ankara, TURKEYIn 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.eninfo:eu-repo/semantics/closedAccessMultifeedback-Layer Neural Networkrecurrent neural networksParticle Swarm Optimizationdynamic system identificationtraining procedureConference Object175172WOS:000336616500044N/A