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Hopfield neural networks for on-line parameter estimation

Authors
Journal
Neural Networks
0893-6080
Publisher
Elsevier
Publication Date
Volume
22
Issue
4
Identifiers
DOI: 10.1016/j.neunet.2009.01.015
Keywords
  • On-Line Parameter Estimation
  • Hopfield Neural Networks
  • Lyapunov Stability Theory

Abstract

Abstract This paper addresses the problem of using Hopfield Neural Networks (HNNs) for on-line parameter estimation. As presented here, a HNN is a nonautonomous nonlinear dynamical system able to produce a time-evolving estimate of the actual parameterization. The stability analysis of the HNN is carried out under more general assumptions than those previously considered in the literature, yielding a weaker sufficient condition under which the estimation error asymptotically converges to zero. Furthermore, a robustness analysis is made, showing that, under the presence of perturbations, the estimation error converges to a bounded neighbourhood of zero, whose size decreases with the size of the perturbations. The results obtained are illustrated by means of two case studies, where the HNN is compared with two other methods.

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