Training ANFIS by using the artificial bee colony algorithm

In this study, a new adaptive network-based fuzzy inference system (ANFIS) training algorithm, the artificial bee colony (ABC) algorithm, is presented. Antecedent and conclusion parameters existing in the structure of ANFIS are optimized with the ABC algorithm and ANFIS training is realized. Identification of a set of nonlinear dynamic systems is performed in order to analyze the suggested training algorithm. The ABC algorithm is operated 30 times for each identification case and the average root mean square error (RMSE) value is obtained. Training RMSE values calculated for the four examples considered are 0.0325, 0.0215, 0.0174, and 0.0294, respectively. In addition, test error values for the same cases are respectively computed as 0.0270, 0.0186, 0.0167, and 0.0435. The results obtained are compared with those of known neuro-fuzzy-based methods frequently used in the literature in identification studies of nonlinear systems. It is shown that ANFIS can be trained successfully by using the ABC algorithm for the identification of nonlinear systems.

PDF

___

[1] Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. IEEE T Syst Man Cyb 1993; 23: 665-685.
[2] Jang JSR, Sun CT, Mizutani E. Neuro fuzzy and soft computing a computational approach to learning and machine intelligence. IEEE T Automat Contr 1997; 42: 1482-1484.
[3] Ho WH, Chen JX, Lee I, Su HC. An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm. Expert Syst Appl 2011; 38: 13050-13056.
[4] Nariman-Zadeh N, Darvizeh A, Dadfarmai MH. Design of ANFIS networks using hybrid genetic and SVD methods for the modeling of explosive cutting process. J Mater Process Tech 2004; 155: 1415-1421.
[5] Chen MY. A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Inform Sci 2013; 220: 180-195.
[6] Shoorehdeli MA, Teshnehlab M, Sedigh AK. Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter. Fuzzy Set Syst 2009; 160: 922-948.
[7] Shoorehdeli MA, Teshnehlab M, Sedigh AK, Khanesar MA. Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods. Appl Soft Comput 2009; 9: 833-850.
[8] Jalali-Heravi M, Asadollahi-Baboli M. Quantitative structure-activity relationship study of serotonin (5-HT7) receptor inhibitors using modified ant colony algorithm and adaptive neuro-fuzzy interference system (ANFIS). Eur J Med Chem 2009; 44: 1463-1470.
[9] Khazraee SM, Jahanmiri AH, Ghorayshi SA. Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution. Neural Comput Appl 2011; 20: 239-248.
[10] Priyadharsini SS, Rajan ES, Sheniha FS. A novel approach for the elimination of artefacts from EEG signals employing an improved artificial immune system algorithm. J Exp Theor Artif In 2016; 28: 239-259.
[11] Karaboga D, Kaya E. Training ANFIS using artificial bee colony algorithm. In: 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications; 1921 June 2013; Albena, Bulgaria. New York, NY, USA: IEEE. pp. 1-5.
[12] Karaboga D, Kaya E. Training ANFIS using artificial bee colony algorithm for nonlinear dynamic systems identification. In: IEEE 2014 Conference on Signal Processing and Communications Applications; 2325 April 2014; Trabzon, Turkey. New York, NY, USA: IEEE. pp. 493-496.
[13] Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-TR06. Kayseri, Turkey: Erciyes University, 2005.
[14] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 2007; 39: 459-471.
[15] Kockanat S, Karaboga N. The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature. Artif Intell Rev 2015; 44: 265-287.
[16] Kockanat S, Karaboga N. A novel 2D-ABC adaptive filter algorithm: a comparative study. Digit Signal Process 2015; 40: 140-153.
[17] Akay B, Karaboga D. A survey on the applications of artificial bee colony in signal, image, and video processing. SIViP 2015; 9: 967-990.
[18] Karaboga D, Gorkemli B, Ozturk C, Karaboga N. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 2014; 42: 21-57.
[19] Li B, Li Y, Gong L. Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model. Eng Appl Artif Intel 2014; 27: 70-79.
[20] Juang CF, Lin YY, Tu CC. A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing. Fuzzy Set Syst 2010; 161: 2552-2568.
[21] Ning W, Yue T, Shao-Man L. A generalized online self-organizing fuzzy neural network for nonlinear dynamic system identification. In: Proceedings of the 30th Chinese Control Conference; 2224 July 2011; Yantai, China. New York, NY, USA: IEEE. pp. 2879-2883.