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Convergence behaviour of connectionist models in large scale diagnostic problems

  • Pattern Recognition
  • Conference paper
  • First Online:
Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 1992)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 604))

Abstract

Though artificial neural networks have been successfully applied for diagnostic problems of different natures, difficulties arise by applying this approach, especially the most frequently used back propagation (BP) procedure, for large scale technical diagnostic problems. Therefore, some acceleration techniques of the BP procedure are described and investigated in the paper. Some network models isomorphic to conventional pattern recognition techniques are also enumerated, with special emphasis on the Condensed Nearest Neighbour Network (CNNN), which is a new, self-organizing network model with supervised learning ability. The surveyed techniques are analyzed and compared on a diagnostic problem with more than 300 pattern features.

During the period 1.8.1990 – 31.12.1991 with a Humboldt Research Fellowship on the leave from: Computer and Automation Institute, Hungarian Academy of Sciences, Kende u. 13–17, Budapest, H-1518 Hungary

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Authors and Affiliations

  1. Institute of Electrical Measurement, University of Paderborn, Pohlweg 47-49, P.O.Box 1621, D-4790, Paderborn

    Laszlo Monostori & Achim Bothe

Authors
  1. Laszlo Monostori
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  2. Achim Bothe
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Fevzi Belli Franz Josef Radermacher

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© 1992 Springer-Verlag Berlin Heidelberg

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Monostori, L., Bothe, A. (1992). Convergence behaviour of connectionist models in large scale diagnostic problems. In: Belli, F., Radermacher, F.J. (eds) Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. IEA/AIE 1992. Lecture Notes in Computer Science, vol 604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024962

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  • DOI: https://doi.org/10.1007/BFb0024962

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  • Print ISBN: 978-3-540-55601-5

  • Online ISBN: 978-3-540-47251-3

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