Hopfield neural networks for on-line parameter estimation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2009

Autores

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

Revista

Título: Neural Networks

Vol. 22 Nº 4

Páginas: 450-462

ISSN: 0893-6080

Editora: Elsevier

Indexação

ISI Web of Knowledge - 32 Citações

ISI Web of Science

Scopus - 45 Citações

COMPENDEX

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13