Dynamical aspects of multi-time scale unsupervised neural networks
Anke Meyer‐Baese, Shantanu Joshi, Helge Joachim Ritter · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
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.