Mean square global asymptotic stability of stochastic recurrent neural networks with distributed delays

By constructing suitable Lyapunov functionals and combining with matrix inequality technique, a new simple sufficient condition is presented for the global asymptotic stability in the mean square of delayed neural networks.

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Bibliographic Details
Published in:Applied mathematics and computation 2009-09, Vol.215 (2), p.791-795
Main Author: Guo, Yingxin
Format: Article
Language:English
Subjects:
Exact sciences and technology
Finite differences and functional equations
Global asymptotic stability
Lyapunov functionals
Mathematical analysis
Mathematics
Matrix inequality
Numerical analysis
Numerical analysis. Scientific computation
Sciences and techniques of general use
Stochastic neural networks with delays
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Description
Summary:By constructing suitable Lyapunov functionals and combining with matrix inequality technique, a new simple sufficient condition is presented for the global asymptotic stability in the mean square of delayed neural networks.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2009.06.002