Fuzzy identification of systems based on adaptive neurons-Reference-Cited by-同舟云学术

Fuzzy identification of systems based on adaptive neurons

Published:2021-03-27 Issue: Volume: Page:1-13
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Ramírez-Mendoza Abigail María Elena¹,Yu Wen²,Li Xiaoou³

Affiliation:

1. 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

2. Department of Automatic Control, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN ). Av. Instituto Politécnico Nacional 2508, San Pedro Zacatenco, C.P. 07360, Mexico City, México

3. 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

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.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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