Please use this identifier to cite or link to this item:
|Title:||Particle swarm based approach of a real-time discrete neural identifier for linear induction motors|
|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.|
|Appears in Collections:||Producción científica UdeG (prueba)|
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.