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
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.
Açıklama
10th International Conference on Electronics, Computer and Computation (ICECCO) -- NOV 07-09, 2013 -- Turgut Ozal Univ, Ankara, TURKEY
Anahtar Kelimeler
Multifeedback-Layer Neural Network, recurrent neural networks, Particle Swarm Optimization, dynamic system identification, training procedure
Kaynak
2013 International Conference on Electronics, Computer and Computation (Icecco)
WoS Q Değeri
N/A
