Robust stability of Cohen-Grossberg neural networks via state transmission matrix
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Networked synchronization control of coupled dynamic networks with time-varying delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability of impulsive Hopfield neural networks with Markovian switching and time-varying delays
International Journal of Applied Mathematics and Computer Science - Semantic Knowledge Engineering
State estimation of markovian jump neural networks with mixed time delays
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
H∞ filtering of markovian jumping neural networks with time delays
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.