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Multi-valued and Universal Binary Neurons: Learning Algorithms, Application to Image Processing and Recognition

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Machine Learning and Data Mining in Pattern Recognition (MLDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1715))

Abstract

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defined multiple-valued function on the single MVN and an arbitrary mapping described by partial-defined or fully-defined Boolean function (which can be not threshold) on the single UBN. The fast-converged learning algorithms are existing for both types of neurons. Such features of the MVN and UBN may be used for solution of the different kinds of problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN) for solution of the image processing and image analysis problems. Another effective application of the MVN is their use as the basic neurons in the neural networks oriented to the image recognition.

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Authors and Affiliations

  1. Minaiskaya 28, kv. 49, Uzhgorod, 294015, Ukraine

    Igor N. Aizenberg & Naum N. Aizenberg

  2. Paustovskogo 3, kv.430, Moscow, Russia

    Georgy A. Krivosheev

Authors
  1. Igor N. Aizenberg
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  2. Naum N. Aizenberg
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  3. Georgy A. Krivosheev
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Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Arno-Nitzsche-Str. 45, D-04277, Leipzig, Germany

    Petra Perner

  2. School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Guilford, GU2 5XH, UK

    Maria Petrou

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© 1999 Springer-Verlag Berlin Heidelberg

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Aizenberg, I.N., Aizenberg, N.N., Krivosheev, G.A. (1999). Multi-valued and Universal Binary Neurons: Learning Algorithms, Application to Image Processing and Recognition. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_3

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  • DOI: https://doi.org/10.1007/3-540-48097-8_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66599-1

  • Online ISBN: 978-3-540-48097-6

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