Hopfield neural networks for on-line parameter estimation

• Type: Article in International Scientific Journal
• Date: 2009

Authors

Hugo Alonso (Author) FEUP; Teresa Mendonca (Author) FCUP; Paula Rocha (Author) FEUP

Journal

Title: Neural Networks

Vol. 22 No. 4

Pages: 450-462

ISSN: 0893-6080

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 32 Citations

ISI Web of Science

Scopus - 45 Citations

COMPENDEX

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-003-KJC

DOI: 10.1016/j.neunet.2009.01.015

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13