Journal
NEUROCOMPUTING
Volume 74, Issue 4, Pages 638-645Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2010.10.001
Keywords
Recurrent neural networks; Lagrange stability; Global exponential attractivity; Delays
Categories
Funding
- Natural Science Foundations of China [60874110, 60974021]
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In this paper, the global exponential stability in Lagrange sense for continuous neutral type recurrent neural networks (NRNNs) with multiple time delays is studied. Three different types of activation functions are considered, including general bounded and two types of sigmoid activation functions. By constructing appropriate Lyapunov functions, some easily verifiable criteria for the ultimate boundedness and global exponential attractivity of NRNNs are obtained. These results can be applied to monostable and multistable neural networks as well as chaos control and chaos synchronization. (c) 2010 Elsevier B.V. All rights reserved.
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