Training the multifeedback-layer neural network using the Particle Swarm Optimization algorithm
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Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE Computer Society
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 -- 7 November 2013 through 8 November 2013 -- Ankara -- 102696
Anahtar Kelimeler
dynamic system identification, Multifeedback-Layer Neural Network, Particle Swarm Optimization, recurrent neural networks, training procedure
Kaynak
2013 International Conference on Electronics, Computer and Computation, ICECCO 2013
