Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors. (29th December 2013)

• Record Type: Journal Article
• Authors: Alanis, Alma Y.; Rangel, E.; Rivera, J.; Arana-Daniel, N.; Lopez-Franco, C.
• Other Names: Yan Jun-Juh Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-12-29
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/715094
• Languages: English
• ISSNs: 1024-123X

Abstract

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 con figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.