Using Neural Networks in a Parallel Adaptative Algorithm for the System Identification Optimization

Authors:
Juan A. Pulido;Juan M. Pérez;Miguel A. Rodríguez
Affiliations:
Departamento de Informática. Escuela Politécnica, Campus Universitario s/n, Cáceres, Spain 10071;Departamento de Informática. Escuela Politécnica, Campus Universitario s/n, Cáceres, Spain 10071;Departamento de Informática. Escuela Politécnica, Campus Universitario s/n, Cáceres, Spain 10071
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

In this work1we present the use of neural networks to implement processing units of a parallel adaptative algorithm for high precision system identification. The proposed algorithm uses recursive least squares processing and ARMAX modeling. After explaining the algorithm and the tunning of its parameters, we show the system identification for four benchmarks with different implementations of this algorithm, demonstrating how neural networks improve the result precision.