Self-organising Artifical Neural Networks

Abstract

Self-organisation is displayed during the unsupervised learning of the Kohonen Neural Network (KNN) algorithm. Classical Markov techniques, the ordinary differential equation (ODE) method being included among these, have so far not yielded a complete general analysis of self-organisation in the KNN. In order to obtain a more general understanding of self-organising behaviour which could then be applied to the analysis of self-organisation in the KNN two simpler self-organising algorithms are described. The first algorithm is based on a simple intuitive understanding of self-organisation. The second is based on a simplification of the KNN algorithm. Using the ODE method general results on the self-organising abilities of the two algorithms are given.KeywordsGain FunctionNeighbourhood FunctionWinner NeuronStochastic Approximation MethodOrganise ConfigurationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.