Real–time Discrete Nonlinear Identification via Recurrent High Order Neural Networks

Identificación No Lineal en Tiempo Real usando Redes Neuronales Recurrentes de Alto Orden

Alma Y. Alanis¹, Edgar N. Sanchez² and Alexander G. Loukianov²

¹CUCEI, Universidad de Guadalajara, Apartado Postal 51–71, Col. Las Aguilas, C.P. 45080, Zapopan, Jalisco, Mexico.

²CINVESTAV, Unidad Guadalajara, Apartado Postal 31–438, Plaza La Luna, Guadalajara, Jalisco, C.P. 45091, Mexico. E–mail: almayalanis@gmail.com

Article received on November 25, 2008
Accepted on March 23, 2009

Abstract

This paper deals with the discrete–time nonlinear system identification via Recurrent High Order Neural Networks, trained with an extended Kalman filter (EKF) based algorithm. The paper also includes the respective stability analysis on the basis of the Lyapunov approach for the whole scheme. Applicability of the scheme is illustrated via real–time implementation for a three phase induction motor.

Keywords: Neural identification, Extended Kalman filtering learning, Discrete–time nonlinear systems, Three phase induction motor.

Resumen

Este artículo trata el problema de identificación de sistemas no lineales discretos usando redes neuronales recurrentes de alto orden entrenadas con un algoritmo basado en el filtro de Kalman extendido (EKF). El artículo también incluye el análisis de estabilidad para el sistema completo, en las bases de la técnica de Lyapunov. La aplicabilidad del esquema se ilustra a través de la implementación en tiempo real para un motor de inducción trifásico.

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Acknowledgement

The authors thank the support of PROMEP/103.5/09/3912 and CONACYT Mexico, through Project 103191Y. They also thank the very useful comments of the anonymous reviewers, which help to improve the paper.

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