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IEEE Transactions on Neural Networks
Article . 1999 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Article . 2018
Data sources: DBLP
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Estimate of exponential convergence rate and exponential stability for neural networks

Authors: Zhang Yi 0001; Pheng-Ann Heng; Ada Wai-Chee Fu;

Estimate of exponential convergence rate and exponential stability for neural networks

Abstract

Estimate of exponential convergence rate and exponential stability are studied for a class of neural networks which includes the Hopfield neural networks and the cellular neural networks. Both local and global exponential convergence is discussed. Theorems for estimate of exponential convergence rate are established and the bounds on the rate of convergence are given. The domains of attraction in the case of local exponential convergence are obtained. Simple conditions are presented for checking exponential stability of the neural networks.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
86
Top 10%
Top 1%
Top 10%
bronze