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On-Line Learning Fokker-Planck Machine

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Abstract

In this letter we present an on-line learning version of the Fokker-Planck machine. The method makes use of a regularized constrained normalized LMS algorithm in order to estimate the time-derivative of the parameter vector of a radial basis function network. The RBF network parametrizes a transition density which satisfies a Fokker-Planck equation, associated to continuous simulated annealing. On-line learning using the constrained normalized LMS method is necessary in order to make the Fokker-Planck machine applicable to large scale nonlinear optimization problems.

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Authors and Affiliations

  1. Department of Electrical Engineering, ESAT-SISTA, Katholieke Universiteit Leuven, Kardinaal Mercierlaan 94, B-3001, Leuven (Heverlee), Belgium E-mail

    J.A.K. Suykens, H. Verrelst & J. Vandewalle

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  1. J.A.K. Suykens
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  2. H. Verrelst
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  3. J. Vandewalle
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Suykens, J., Verrelst, H. & Vandewalle, J. On-Line Learning Fokker-Planck Machine. Neural Processing Letters 7, 81–89 (1998). https://doi.org/10.1023/A:1009632428145

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  • DOI: https://doi.org/10.1023/A:1009632428145

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