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Local and global stability analysis of an unsupervised competitive neural network
1Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310-6046, USA. amb@eng.fsu.edu
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.
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