A HYBRID METHOD OF MODIFIED CAT SWARM OPTIMIZATION AND GRADIENT DESCENT ALGORITHM FOR TRAINING ANFIS MEYSAM OROUSKHANI Department of Computer Engineering Science and Research Branch Islamic Azad University, Tehran, Iran orouskhani@ce.sharif.edu MOHAMMAD MANSOURI Intelligent System Laboratory (ISLAB) Electrical and Computer Engineering Department K. N. Toosi University, Tehran, Iran YASIN OROUSKHANI * Department of Computer Engineering Sharif University of Technology, Tehran, Iran MOHAMMAD TESHNEHLAB Industrial Control Center of Excellence Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology, Tehran, Iran Received 31 March 2012 Revised 20 December 2012 Published 18 June 2013 This paper introduces a novel approach for tuning the parameters of the adaptive network- based fuzzy inference system (ANFIS). In the commonly used training methods, the antecedent and consequent parameters of ANFIS are trained by gradient-based algorithms and recursive least square method, respectively. In this study, a new swarm-based meta-heuristic optimization algorithm, so-called \Cat Swarm Optimization", is used in order to train the antecedent part parameters and gradient descent algorithm is applied for training the consequent part para- meters. Experimental results for prediction of MackeyGlass model and identi¯cation of two nonlinear dynamic systems reveal that the performance of proposed algorithm is much better and it shows quite satisfactory results. Keywords: Cat swarm optimization; ANFIS; swarm intelligence; prediction and identi¯cation. *BSc Student of Computer Engineering. International Journal of Computational Intelligence and Applications Vol. 12, No. 2 (2013) 1350007 (15 pages) # . c Imperial College Press DOI: 10.1142/S1469026813500077 1350007-1 Int. J. Comp. Intel. Appl. 2013.12. Downloaded from www.worldscientific.com by Mr. Meysam Orouskhani on 10/24/14. For personal use only.