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Proceedings Paper

Alternation minimization training of radial basis function networks
Author(s): Peter T. Szymanski; Michael D. Lemmon
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Paper Abstract

Radial basis function (RBF) networks and their training have garnered much interest in the neural computing literature. Current training methods resemble Expectation-Maximization (EM) techniques and optimize network performance by alternately minimizing performance measures with respect to disjoint parameter sets. Since the number of RBF parameters and the training data sets may be large, efficient training methods are imperative for these networks' practical use. This paper presents two hybrid interior point (IP) training algorithms based upon solvers for linear programming (LP) problems. Presented are small-step and large-step path following algorithms which implement an alternating minimization. The small-step algorithm converges to (epsilon) -neighborhoods of locally optimal solutions with a theoretical O((root)nlog2((root)n/(epsilon) )) iteration rate and O(n3.5log2((root)n/(epsilon) )) floating point operation (flop) cost. It exhibits an empirical cost of approximately O(n2log2(1/(epsilon) )) flops where n is the dimension of an associated LP problem. The large-step algorithm exhibits a constant convergence rate and approximately O(n1.5log2(1/(epsilon) )) flop cost. IP/EM algorithm comparison show that the IP methods exhibit lower order computational properties, and that for some problems the large-step algorithm outperforms the EM algorithm. Also presented are results demonstrating the small-step algorithm's use for training on a system identification problem.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235923
Show Author Affiliations
Peter T. Szymanski, Univ. of Notre Dame (United States)
Michael D. Lemmon, Univ. of Notre Dame (United States)

Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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