Research article summary (published 19 Mar 2009):

Hopfield neural networks for on-line parameter estimation.

Full 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.

Author information

Author/s: Alonso, Hugo (H); Mendonça, Teresa (T); Rocha, Paula (P);

Affiliation: Unidade de Investigação Matemática e Aplicações, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. hugo.alonso(-atsign-)ua.pt

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Neural networks : the official journal of the International Neural Network Society (Neural Netw), published in United States. (Language: eng)

Reference: 2009-May; vol 22 (issue 4) : pp 450-62

Dates: Created 2009/05/25; Completed 2009/08/21;

PMID: 19386467, status: MEDLINE (last retrieval date: 8/21/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms Artificial Intelligence Computer Simulation Decision Making, Computer-Assisted

Decision Making, Computer-Assisted Mathematical Concepts Neural Networks (Computer) Pattern Recognition, Automated

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