Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification
Öz
In this study, the premise and consequent parameters
of ANFIS are optimized using Genetic Algorithm (GA) based on a
population algorithm. The proposed approach is applied to the nonlinear
dynamic system identification problem. The simulation results of the
method are compared with the Backpropagation (BP) algorithm and the
results of other methods that are available in the literature. With this
study it was observed that the optimisation of ANFIS parameters using
GA is more successful than the other methods.
Anahtar Kelimeler
Kaynakça
- D. Karaboga and E. Kaya, “Training ANFIS using artificial bee colony algorithm for nonlinear dynamic system identification,” in: IEEE 22nd Signal Processing and Communications Applications Conference (SIU), 2014, pp. 493-496.
- P. Liu, W. Leng and W. Fang, “Training ANFIS model with an improved quantum-behaved particle swarm optimization algorithm,” Mathematical Prob. in Eng. vol. 2013, 2013.
- V.S. Ghomsheh, M.A. Shoorehdeli and M. Teshnehlab, “Training ANFIS structure with modified PSO algorithm,” In Control and Automation, Med’07. Mediterranean Conference on IEEE, 2007, pp. 1-6.
- M.A. Shoorehdeli, M. Teshnehlab, A.K. Sedigh and M.A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Applied Soft Comput. vol. 9, no. 2, pp. 833–850, 2009.
- M.A. Shoorehdeli, M. Teshnehlab and A.K. Sedigh, “Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter,” Fuzzy Sets and Systems. vol. 160, pp. 922–948, 2009.
- D. Simon, “Training fuzzy systems with the extended Kalman Filter,” Fuzzy Sets Syst. vol. 132, pp. 189–199, 2002.
- E.G. Carrano, R.H.C. Takahashi, W.M. Caminhas and O.M. Neto, “A genetic algorithm for multiobjective training of ANFIS fuzzy networks,” IEEE Congress on Evolutionary Computation, pp. 3259–3265, 2008.
- F. Cus, J. Balic and U. Zuperl, “Hybrid ANFIS-ants system based optimisation of turning parameters,” Journal of Achievements in Materials. vol. 36, no. 1, pp. 79-86, 2009.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yazarlar
Bülent Haznedar
HASAN KALYONCU ÜNİVERSİTESİ
Türkiye
Adem Kalınlı
ERCİYES ÜNİVERSİTESİ
Türkiye
Yayımlanma Tarihi
26 Aralık 2016
Gönderilme Tarihi
17 Kasım 2016
Kabul Tarihi
1 Aralık 2016
Yayımlandığı Sayı
Yıl 2016 Cilt: 4 Sayı: Special Issue-1
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