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Muster-Assoziation mit Time-delayed Networks

  • Conference paper
Mustererkennung 1992

Part of the book series: Informatik aktuell ((INFORMAT))

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Zusammenfassung

In diesem Beitrag wird ein Verfahren zur seriellen Muster-Assoziation in neuronaler Architektur vorgeschlagen. Die time-delayed Netzwerk-Architektur wird zunächst auf serielle Muster-Assoziation einfacher geometrischer Muster angewandt. Musterinduzierte Übergänge von Laut-Folgen in der Spracherkennung werden ebenso diskutiert.

Abstract

In this contribution we propose a method for pattern association in neural architecture. We apply the technique of time-delayed networks. We study the serial pattern association of simple geometrical patterns. Pattern induced transitions in speech recognition are also discussed.

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Referenzen

  1. D. Kleinfeld: Sequential State Generation by Model Neural Networks, Proc. Natl. Acad.Sci. USA 83,9469 (1986).

    Article  MathSciNet  Google Scholar 

  2. H. Sompolinsky, I. Kanter: Temporal Association in Asymmetric Neural Networks, Phys.Rev. Lett. 57,2861(1987).

    Google Scholar 

  3. S. Dehaene, J.P. Changeux, J.V. Nadal: Neural Networks That Learn Temporal Sequences by Selection, Proc.Natl.Acad.Sci. USA 84,2767(1987).

    Article  MathSciNet  Google Scholar 

  4. A. Herz, B. Sulzer, R. Kühn, J.L. van Hemmen: The Hebb Rule: Storing Static and Dynamic Objects in an Associative Neural Network, Europhys. Lett 7, 663(1988).

    Article  Google Scholar 

  5. A. Herz, B. Sulzer, R. Kühn, J.L. van Hemmen: Hebbian Learning Reconsidered, Biol. Cybern. 60. 457(1989).

    Google Scholar 

  6. R. Kühn, J.L. van Hemmen: Temporal Association, in Models of Neural Networks (E. Domany, J.L. van Hemmen, K. Schulten, Eds.), Springer-Verlag, Heidelberg (1991).

    Google Scholar 

  7. J. Buhmann, K. Schulten:Storing Sequences of Biased Patterns in Neural Networks with Stochastic Dynamics, in Neural Computers(R. Eckmiller, G. Hartmann, Hauske, Eds.) North-Holland,Elsevier Science Publishers B.V. Amsterdam (1990).

    Google Scholar 

  8. U. Riedel, R. Kühn, J.L. van Hemmen:Temporal Sequences and Chaos in Neural Nets, Phys.Rev.A,38, 1105(1988).

    Article  MathSciNet  Google Scholar 

  9. S. Faber, Diplomarbeit, Soest (1992).

    Google Scholar 

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© 1992 Springer-Verlag Berlin Heidelberg

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Grauel, A., Grundmann, HG., Pels, R. (1992). Muster-Assoziation mit Time-delayed Networks. In: Fuchs, S., Hoffmann, R. (eds) Mustererkennung 1992. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77785-1_42

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  • DOI: https://doi.org/10.1007/978-3-642-77785-1_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55936-8

  • Online ISBN: 978-3-642-77785-1

  • eBook Packages: Springer Book Archive

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