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Proceedings Paper

Dynamical aspects of multi-time scale unsupervised neural networks
Author(s): Anke Meyer-Bäse; Shantanu Joshi; Helge Ritter
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Paper Abstract

Multi-time scale unsupervised neural networks (MTSUNN) represent an established technique in pattern recognition for feature extraction and cluster analysis. From the nonlinear systems analysis perspective, they implement a very complex coupled multi-mode dynamics. This paper gives a comprehensive overview of several neural architectures of a combined activity and weights dynamics. The global asymptotic and exponential stability of the equilibrium points of these continuous-time recurrent systems whose weights are adapted based on unsupervised learning laws are mathematically analyzed. The derived architectures can lead to hybrid implementations in VLSI techniques.

Paper Details

Date Published: 9 May 2006
PDF: 12 pages
Proc. SPIE 6229, Intelligent Computing: Theory and Applications IV, 62290V (9 May 2006); doi: 10.1117/12.668681
Show Author Affiliations
Anke Meyer-Bäse, Florida State Univ. (United States)
Shantanu Joshi, Florida State Univ. (United States)
Helge Ritter, Bielefeld Univ. (Germany)

Published in SPIE Proceedings Vol. 6229:
Intelligent Computing: Theory and Applications IV
Kevin L. Priddy; Emre Ertin, Editor(s)

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