Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification

Year 2016, , 44 - 47, 26.12.2016

Bülent Haznedar , Adem Kalınlı

https://doi.org/10.18201/ijisae.266053

Cited By: 21

Abstract

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.

Keywords

Neuro-Fuzzy, ANFIS, Genetic Algorithm, System Identification

References

• 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.
• S. Uzundurukan, “Determination and modeling of basic parameters that affect the swelling properties of soils,” Ph.D. Thesis, Suleyman Demirel University, Turkey, 2006.
• J.S.R. Jang and C.T. Sun, “Neuro-Fuzzy modeling and control,” The proceedings of the IEEE. vol. 83, no. 3, 378-406, 1995.
• M. Mitchell, “An Introduction to Genetic Algorithms,” MIT Press, 1998.
• S.Lek, M.Scardi, P.F.M. Verdonschot, J.-P. Descy and Y.-S. Park, Modelling Community Structure in Freshwater Ecosystems. Berlin, Germany: Springer-Verlag, 2005.
• D.E. Rumelhart and J.L. McClelland, “Explorations in the micro-structure of cognition,” parallel distrubuted processing 1. Cambridge, MA: MIT Press, 1986.
• C.F. Juang, “A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms,” IEEE Transactions on Fuzzy Systems. vol. 10, no. 2, pp. 155–170, 2002.
• A. Kalinli, “Training Elman network using simulated annealing algorithm,” Journal of the Institute of Science and Technology of Erciyes University. vol. 19, pp. 28-37, 2003.
• A. Kalinli and D. Karaboga, “Training recurrent neural networks by using parallel tabu search algorithm based on crossover operation,” Engineering Applications of Artificial Intell. vol. 17, pp. 529-542, 2004.