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

Author

Listed:
  • Alma Y. Alanis
  • E. Rangel
  • J. Rivera
  • N. Arana-Daniel
  • C. Lopez-Franco

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.

Suggested Citation

  • Alma Y. Alanis & E. Rangel & J. Rivera & N. Arana-Daniel & C. Lopez-Franco, 2013. "Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, December.
    • Handle: RePEc:hin:jnlmpe:715094
    DOI: 10.1155/2013/715094
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