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Proceedings Paper

Interior-point competitive learning of control agents in colony-style systems
Author(s): Michael D. Lemmon; Peter T. Szymanski; Chris Bett
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Paper Abstract

This paper presents an alternating minimization (AM) algorithm used in training radial basis function (RBF) networks. The AM algorithm can also be viewed as a competitive learning paradigm. Its use is illustrated by optimizing a colony-style control system. The application arises in the context of hybrid control systems. The algorithm is a modification of a small-step interior point method used in solving primal linear programs. The algorithm has a convergence rate of 0((root)nL) iterations where n is a measure of the network size and L is a measure of the resulting solution's accuracy.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205148
Show Author Affiliations
Michael D. Lemmon, Univ. of Notre Dame (United States)
Peter T. Szymanski, Univ. of Notre Dame (United States)
Chris Bett, Univ. of Notre Dame (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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