Adding a conscience to competitive learning

Abstract

There are a number of neural networks that self-organize on the basis of what has come to be known as Kohonen learning. The author introduces a modification of Kohonen learning that provides rapid convergence and improved representation of the input data. In many areas of pattern recognition, statistical analysis, and control, it is essential to form a nonparametric model of a probability density function p(x). The purpose of the improvement to Kohonen learning presented is to form a better approximation of p(x). Simulation results are presented to illustrate the operation of this competitive learning algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>