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Mathematical Problems in Engineering
Volume 2013, Article ID 715094, 9 pages
Research Article

Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. Las Aguilas, Zapopan, Jalisco, Mexico

Received 3 July 2013; Accepted 1 December 2013

Academic Editor: Jun-Juh Yan

Copyright © 2013 Alma Y. Alanis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM) model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN) trained with a novel algorithm based on extended Kalman filter (EKF) and particle swarm optimization (PSO), using an online series-parallel configuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.