Hopfield neural networks for on-line parameter estimation

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...

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Bibliographic Details
Published in:Neural networks 2009-05, Vol.22 (4), p.450-462
Main Authors: Alonso, Hugo, Mendonça, Teresa, Rocha, Paula
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Decision Making, Computer-Assisted
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Hopfield neural networks
Information, signal and communications theory
Lyapunov stability theory
Mathematical Concepts
Neural Networks (Computer)
On-line parameter estimation
Pattern Recognition, Automated
Signal and communications theory
Signal, noise
Telecommunications and information theory
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2009.01.015