Diffusion approximation of frequency sensitive competitive learning
IEEE Transactions on Neural NetworksPublished 1 September 1997
A.S. Galanopoulos, Randolph L. Moses, Stanley C. Ahalt
Citations24
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TL;DR
This paper approximate the final phase of FSCL learning by a diffusion process described by the Fokker-Plank equation and shows that the convergence conditions involve only the learning rate and that they are the same as the conditions for weak convergence described previously.
Abstract
The focus of this paper is a convergence study of the frequency sensitive competitive learning (FSCL) algorithm. We approximate the final phase of FSCL learning by a diffusion process described by the Fokker-Plank equation. Sufficient and necessary conditions are presented for the convergence of the diffusion process to a local equilibrium. The analysis parallels that by Ritter-Schulten (1988) for Kohonen's self-organizing map. We show that the convergence conditions involve only the learning rate and that they are the same as the conditions for weak convergence described previously. Our analysis thus broadens the class of algorithms that have been shown to have these types of convergence characteristics.
Keywords
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