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GLOBAL STABILITY ANALYSIS FOR A CLASS OF COHEN-GROSSBERG NEURAL NETWORK MODELS
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 Title & Authors
GLOBAL STABILITY ANALYSIS FOR A CLASS OF COHEN-GROSSBERG NEURAL NETWORK MODELS
Guo, Yingxin;
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 Abstract
By constructing suitable Lyapunov functionals and combining with matrix inequality technique, a new simple sufficient condition is presented for the global asymptotic stability of the Cohen-Grossberg neural network models. The condition contains and improves some of the previous results in the earlier references.
 Keywords
delay differential equations;Lyapunov functionals;matrix inequality;global asymptotic stability;
 Language
English
 Cited by
1.
Asymptotic and Robust Mean Square Stability Analysis of Impulsive High-Order BAM Neural Networks with Time-Varying Delays, Circuits, Systems, and Signal Processing, 2017, 1531-5878  crossref(new windwow)
2.
Globally Robust Stability Analysis for Stochastic Cohen–Grossberg Neural Networks with Impulse Control and Time-Varying Delays, Ukrainian Mathematical Journal, 2018, 69, 8, 1220  crossref(new windwow)
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