Qualitative Analysis and Synthesis of Recurrent Neural Networks
Qualitative Analysis and Synthesis of Recurrent Neural Networks
New conditions on global stability of Cohen-Grossberg neural networks
Neural Computation
New Results on the Robust Stability of Cohen---Grossberg Neural Networks with Delays
Neural Processing Letters
Journal of Computational and Applied Mathematics
Robust Stability in Cohen---Grossberg Neural Network with both Time-Varying and Distributed Delays
Neural Processing Letters
Robust Stability Criterion for Delayed Neural Networks with Discontinuous Activation Functions
Neural Processing Letters
Improved global robust asymptotic stability criteria for delayed cellular neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Global exponential stability of Cohen-Grossberg neural network with time varying delays
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Stability analysis for Cohen-Grossberg neural networks with time-varying delays via LMI approach
Expert Systems with Applications: An International Journal
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Novel stability criteria of Cohen–Grossberg neural networks with time-varying delays
International Journal of Circuit Theory and Applications
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This paper considers the problem of robust stability analysis of Cohen-Grossberg neural networks with time-varying delays and norm-bounded parameter uncertainties. The activation functions are assumed to be bounded and globally Lipschitz continuous. Both the monotonic increasing and non-monotonic increasing activations are considered. In terms of linear matrix inequalities (LMIs), sufficient conditions are obtained by using the Lyapunov-Krasovskii method, which guarantee the existence, uniqueness and global robust asymptotic stability of the equilibrium point of the delayed Cohen-Grossberg neural network. It is theoretically established that the derived LMI conditions are less conservative than certain existing ones in the literature. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI conditions.