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Fuzzy-neunet: A non standard neural network

  • Neural Network Architectures And Algorithms
  • Conference paper
  • First Online:
Artificial Neural Networks (IWANN 1991)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 540))

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Abstract

Most of today's connectionist networks hold the information in the weights of the connections, the synapses (see Error Back Propagation, Hopfield-Net, Neocognitron). In contrast to these models NEUNET is a fully self organizing network. Its information is represented only in its overall structure, which is adopted dynamically through new ‘experiences’ and a special type of persistent activation-states (the so-called stamps) of the units.

The goal of this paper is to give an overview of the NEUNET-algorithms as well as of its theoretical background. The main part is dedicated to a new probability-based approach which does significantly improve the capabilities of NEUNET. Some characteristic examples are given for illustrating applications in pattern recognition with autoassociative recall. In addition to a presentation of the current state of development of NEUNET a description of prospects of future work is given.

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References

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Authors and Affiliations

  1. Institut für Informatik, Johannes Kepler University, Altenbergerstr. 69, A-4040, Linz, Austria

    Paul Andlinger & Ernst R. Reichl

Authors
  1. Paul Andlinger
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  2. Ernst R. Reichl
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Editor information

Alberto Prieto

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© 1991 Springer-Verlag Berlin Heidelberg

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Andlinger, P., Reichl, E.R. (1991). Fuzzy-neunet: A non standard neural network. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035892

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  • DOI: https://doi.org/10.1007/BFb0035892

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54537-8

  • Online ISBN: 978-3-540-38460-1

  • eBook Packages: Springer Book Archive

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