Convergence in a learning network with pattern feedback

  • L. Masih
  • T. J. Stonham
Adaptive Learning Networks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)


An architecture based on networks of logic functions is proposed with the output of the network forming a model or archetype image of the class of pattern stimulating the net. Frequency of occurrence information which relates the output of each function to the number of times the stimulus sampled in the test pattern has occurred during the training phase is used. This is shown to have significant advantages over simple binary systems and networks with progressively decreasing connectivity during training-the so called 'ageing 'effect. The frequency of occurrence networks are no longer sensitive to the sequence or order of the training categories and have a greatly improved noise stability. The network is assessed with real world data made up of some hundreds of examples of machine printed numerals.


Real World Data Output Image Internal Image Binary Output External Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Fairhurst, M.C., ‘Natural pattern clustering in digital learning nets'. Electronic letters, 1971, Vol. 7, P724.Google Scholar
  2. 2.
    Stonham, T.J., Wilkie, B.A. and Masih, L., ‘Higher order Adaptive Networks-Some aspects of multi-class and feedback systems'. Proceeding of the Third Alvey Vision Conference, Cambridge, 1987, P245.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • L. Masih
    • 1
  • T. J. Stonham
    • 1
  1. 1.Department of Electrical EngineeringBrunel UniversityUxbridge

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