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Expert Mixture Methods for Adaptive Channel Equalization

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Book cover Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 (ICANN 2003, ICONIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

Abstract

Mixture of expert algorithms are able to achieve a total loss close to the total loss of the best expert over a sequence of examples. We consider the use of mixture of expert algorithms applied to the signal processing problem of channel equalization. We use these mixture of expert algorithms to track the best parameter settings for equalizers in the presence of noise or when the channel characteristics are unknown, maybe non-stationary. The experiments performed demonstrate the use of expert algorithms in tracking the best LMS equalizer step size in the presence of additive noise and in prior selection for the approximate natural gradient (ANG) algorithm.

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Author information

Authors and Affiliations

  1. Research School of Information Sciences and Engineering, The Australian National University, Canberra, ACT, 0200

    Edward Harrington

Authors
  1. Edward Harrington
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Editor information

Editors and Affiliations

  1. Bogazici University, Bebek, 34342, Istanbul, Turkey

    Okyay Kaynak  & Ethem Alpaydin  & 

  2. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O.B. 5400, 02015, Finland

    Erkki Oja

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Lei Xu

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© 2003 Springer-Verlag Berlin Heidelberg

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Harrington, E. (2003). Expert Mixture Methods for Adaptive Channel Equalization. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_64

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  • DOI: https://doi.org/10.1007/3-540-44989-2_64

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

  • eBook Packages: Springer Book Archive

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