Using neural networks in a parallel adaptative algorithm for the system identification optimization

Abstract

In this work we 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. © Springer-Verlag Berlin Heidelberg 2003.