Local and global 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 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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Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310-6046, USA. amb@eng.fsu.edu
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D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016247:Information Storage and Retrieval; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated