Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification

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.

[1]  Dan Simon,et al.  Training fuzzy systems with the extended Kalman filter , 2002, Fuzzy Sets Syst..

[2]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[3]  M. Teshnehlab,et al.  Training ANFIS structure with modified PSO algorithm , 2007, 2007 Mediterranean Conference on Control & Automation.

[4]  Peilin Liu,et al.  Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm , 2013 .

[5]  Ricardo H. C. Takahashi,et al.  A genetic algorithm for multiobjective training of ANFIS fuzzy networks , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[6]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[7]  Chia-Feng Juang,et al.  A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms , 2002, IEEE Trans. Fuzzy Syst..

[8]  Young-Seuk Park,et al.  Modelling Community Structure in Freshwater Ecosystems , 2014 .

[9]  Dervis Karaboga,et al.  Training recurrent neural networks by using parallel tabu search algorithm based on crossover operation , 2004, Eng. Appl. Artif. Intell..

[10]  Jože Balič,et al.  Hybrid ANFIS-ants system based optimisation of turning parameters , 2009 .

[11]  Dervis Karaboga,et al.  Training ANFIS using artificial bee colony algorithm , 2013, 2013 IEEE INISTA.

[12]  Mojtaba Ahmadieh Khanesar,et al.  Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods , 2009, Appl. Soft Comput..

[13]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[14]  Adem Kalinli.  TRAINING ELMAN NETWORK USING SIMULATED ANNEALING ALGORITHM , 2003 .

[15]  Mohammad Teshnehlab,et al.  Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter , 2009, Fuzzy Sets Syst..