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Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli

โœ Scribed by Zhigang Zeng; Jun Wang

Publisher
Elsevier Science
Year
2006
Tongue
English
Weight
1020 KB
Volume
19
Category
Article
ISSN
0893-6080

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โœฆ Synopsis

This paper presents new theoretical results on the global exponential stability of recurrent neural networks with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown that the Cohen-Grossberg neural network is globally exponentially stable, if the absolute value of the input vector exceeds a criterion. As special cases, the Hopfield neural network and the cellular neural network are examined in detail. In addition, it is shown that criteria herein, if partially satisfied, can still be used in combination with existing stability conditions. Simulation results are also discussed in two illustrative examples.

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