Research Article
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Year 2016, , 44 - 47, 26.12.2016
https://doi.org/10.18201/ijisae.266053

Abstract

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.

Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification

Year 2016, , 44 - 47, 26.12.2016
https://doi.org/10.18201/ijisae.266053

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.

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.
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Bülent Haznedar

Adem Kalınlı

Publication Date December 26, 2016
Published in Issue Year 2016

Cite

APA Haznedar, B., & Kalınlı, A. (2016). Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 44-47. https://doi.org/10.18201/ijisae.266053
AMA Haznedar B, Kalınlı A. Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification. International Journal of Intelligent Systems and Applications in Engineering. December 2016;4(Special Issue-1):44-47. doi:10.18201/ijisae.266053
Chicago Haznedar, Bülent, and Adem Kalınlı. “Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification”. International Journal of Intelligent Systems and Applications in Engineering 4, no. Special Issue-1 (December 2016): 44-47. https://doi.org/10.18201/ijisae.266053.
EndNote Haznedar B, Kalınlı A (December 1, 2016) Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 44–47.
IEEE B. Haznedar and A. Kalınlı, “Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, pp. 44–47, 2016, doi: 10.18201/ijisae.266053.
ISNAD Haznedar, Bülent - Kalınlı, Adem. “Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (December 2016), 44-47. https://doi.org/10.18201/ijisae.266053.
JAMA Haznedar B, Kalınlı A. Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:44–47.
MLA Haznedar, Bülent and Adem Kalınlı. “Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, 2016, pp. 44-47, doi:10.18201/ijisae.266053.
Vancouver Haznedar B, Kalınlı A. Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):44-7.

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