@inproceedings{d65c39032a5a402b81ea2f0f2430ea4e,
title = "Stability analysis of an unsupervised competitive neural network",
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.",
author = "Anke Meyer-Baese and Vera Th{\"u}mmler and Fabian Theis",
year = "2006",
doi = "10.1109/ijcnn.2006.246799",
language = "English",
isbn = "0780394909",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1025--1028",
booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",
note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",
}