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Hopfield neural networks for state estimation: parameters, efficient implementation and results

Neuronale Hopfield-Netzwerke für die Zustandsschätzung: Parameter, wirksame Implementierung und Ergebnisse

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Abstract

State estimation processes measurements and other information to find the network state vector. In this paper, state estimation is considered as an optimization problem to be solved with a Hopfield neural network. Several activation models for this network are simulated and compared. A new method is proposed that calculates the integration step parameter for this network in an autonomous way, eliminating the need for determining it in a manual way for each particular problem. This algorithm has been successfully tested for a wide range of electrical nets. Neural and classic analytical methods are compared.

Zusammenfassung

Die Zustandsschätzung verarbeitet Messungen und andere Informationen, um den Netzzustandsvektor zu finden. Dieser Beitrag betrachtet die Zustandsschätzung als Optimierungsproblem, das mit einem neuronalen Hopfield-Netzwerk gelöst wird. Verschiedene Aktivierungsmodelle für dieses Netzwerk werden simuliert und verglichen. Eine neue Methode, die den Integrationsschrittparameter für dieses Netz autonom berechnet, und die es erübrigt, ihn für jedes einzelne Problem manuell zu bestimmen, wird vorgeschlagen. Dieser Algorithmus ist für einen großen Bereich von elektrischen Netzen erfolgreich getestet worden. Neuronale und klassische analytische Methoden werden verglichen.

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Authors and Affiliations

  1. Dpto. Technología Electrónica E.T.S.I. Telecomunicación, Universidad de Málaga, Campus Teatinos s/n, E-29071, Málaga, Spain

    F. García-Lagos, G. Joya & F. Sandoval

  2. Dpto. Electrónica E.T.S.I. Informática, Universidad de Málaga, Campus Teatinos s/n, E-29071, Málaga, Spain

    F. J. Marín

Authors
  1. F. García-Lagos
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  2. G. Joya
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  3. F. J. Marín
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  4. F. Sandoval
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García-Lagos, F., Joya, G., Marín, F.J. et al. Hopfield neural networks for state estimation: parameters, efficient implementation and results. Elektrotech. Inftech. 117, 4–7 (2000). https://doi.org/10.1007/BF03161391

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  • DOI: https://doi.org/10.1007/BF03161391

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