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On a perceptron-type learning rule for higher order Hopfield neural networks including dilation and translation

Schnelle Fakten

  • Interne Autorenschaft

  • Veröffentlichung

    • 2002
  • Zeitschrift/Zeitung

    Neurocomputing (1-4)

  • Organisationseinheit

  • Fachgebiete

    • Angewandte Informatik
  • Format

    Journalartikel (Artikel)

Zitat

B. Lenze, “On a perceptron-type learning rule for higher order Hopfield neural networks including dilation and translation,” Neurocomputing, vol. 48, no. 1–4, pp. 391–401, 2002.

Abstract

In the following, we will introduce a new Perceptron-like learning rule to enhance the recall performance of higher order Hopfield neural networks without significant increase of their complexity. In detail, our approach will lead to a generalized Perceptron learning rule which generates higher order Hopfield neural networks with dilation and translation that perform perfectly on the training set in case that the latter fulfills the so-called conditionally strong Γ-separability condition. In this sense, our learning scheme satisfies a kind of optimality criterion which means that it finds appropriate network parameters in a finite number of learning cycles in case that a solution exists.

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