Affiliations: [a] Electromechanical Engineering Division, Higher Technological Institute of the West of the State of Hidalgo (ITSOEH for its acronym in Spanish). Paseo del Agrarismo 2000. Carretera Mixquiahuala - Tula, km 2.5, Mixquiahuala de Juárez, Hidalgo, C.P. 42700 | [b] Department of Automatic Control, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN for its acronym in Spanish). Av. Instituto Politécnico Nacional 2508, San Pedro Zacatenco, C.P. 07360, Mexico City, México | [c] Computer Department, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN for its acronym in Spanish). Av. Instituto Politécnico Nacional 2508, San Pedro Zacatenco, C.P. 07360, Mexico City, México
Correspondence:
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Corresponding author. Abigail María Elena Ramírez-Mendoza, Electromechanical Engineering Division, Higher Technological Institute of the West of the State of Hidalgo (ITSOEH for its acronym in Spanish). Paseo del Agrarismo 2000. Carretera Mixquiahuala - Tula, km 2.5, Mixquiahuala de Juárez, Hidalgo, C.P. 42700. E-mails: amramirez@itsoeh.edu.mx, aramirez@cinvestav.mx, aramirezm@ctrl.cinvestav.mx.
Abstract: The identification of nonlinear systems is a complex task. This article presents a method comparison between the new Fuzzy Adaptive Neurons (FAN), Radial Basis Function Network (RBF), and Adaptive Network-Based Fuzzy Inference System (ANFIS). The nonlinear systems presented are solved with stable and optimal learning. The simulation of the results for two models presented, are carried out in Matlab®, the optimization of the system identification for the first and second systems were obtained with great success.
Keywords: Fuzzy adaptive neurons, identification of systems, learning algorithm, level sets, ANFIS, nonlinear systems
