Local and Global Stability Analysis of an Unsupervised Competitive Neural Network

Authors:
A. Meyer-Base;V. Thummler
Affiliations:
Florida State Univ., Tallahassee;-
Venue:
IEEE Transactions on Neural Networks
Year:
2008

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Neurocomputing

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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 multitime scale dynamics is proposed in this letter. The local and 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 multitime scale architectures.