Stability Analysis of an Unsupervised Competitive Neural Network

Unsupervised competitive neural networks (UCNN) are an established technique in pattern recognition for feature extraction and cluster analysis. A novel model of an unsupervised competitive neural network implementing a multi—time scale dynamics is proposed in this paper. The global asymptotic stability of the equilibrium points of this continuous—time recurrent system whose weights are adapted based on a competitive learning law is mathematically analyzed. The proposed neural network and the derived results are compared with those obtained from other multi—time scale architectures.