On-Line Learning Fokker-Planck Machine

Authors:
J. A. K. Suykens;H. Verrelst;J. Vandewalle
Affiliations:
Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-SISTA, Kardinaal Mercierlaan 94, B-3001 Leuven (Heverlee), Belgium E-mail: johan.suykens@esat.kuleuven.ac.be;Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-SISTA, Kardinaal Mercierlaan 94, B-3001 Leuven (Heverlee), Belgium E-mail: johan.suykens@esat.kuleuven.ac.be;Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-SISTA, Kardinaal Mercierlaan 94, B-3001 Leuven (Heverlee), Belgium E-mail: johan.suykens@esat.kuleuven.ac.be
Venue:
Neural Processing Letters
Year:
1998

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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 regularizedconstrained normalized LMS algorithm in order to estimatethe time-derivative of the parameter vector of a radial basis functionnetwork. The RBF network parametrizes a transition densitywhich satisfies a Fokker-Planck equation, associated to continuoussimulated annealing. On-line learning using the constrained normalized LMSmethod is necessary in order to make the Fokker-Planck machine applicable to large scale nonlinear optimization problems.