Abstract
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 ...
Abstract
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.
More literature (via CORE)