Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/43489
Title: Particle swarm based approach of a real-time discrete neural identifier for linear induction motors
Author: Alanis, A.Y.
Rangel, E.
Rivera, J.
Arana-Daniel, N.
Lopez-Franco, C.
Issue Date: 2013
Abstract: 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. � 2013 Alma Y. Alanis et al.
URI: http://www.scopus.com/inward/record.url?eid=2-s2.0-84896119309&partnerID=40&md5=7da10b6e4782f9d39c34387aa6ed0ef2
http://hdl.handle.net/20.500.12104/43489
Appears in Collections:Producción científica UdeG

Files in This Item:
There are no files associated with this item.


Items in RIUdeG are protected by copyright, with all rights reserved, unless otherwise indicated.