Global convergence rate of recurrently connected neural networks

Authors:
Tianping Chen;Wenlian Lu;Shun-ichi Amari
Affiliations:
Laboratory of Nonlinear Mathematics Science, Institute of Mathematics, Fudan University, Shanghai, China;Laboratory of Nonlinear Mathematics Science, Institute of Mathematics, Fudan University, Shanghai, China;RIKEN Brain Science Institute, Wako-shi, Saitama 351-01, Japan
Venue:
Neural Computation
Year:
2002

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Abstract

We discuss recurrently connected neural networks, investigating their global exponential stability (GES). Some sufficient conditions for a class of recurrent neural networks belonging to GES are given. Sharp convergence rate is given too.