Exponential periodicity and stability of delayed neural networks
Mathematics and Computers in Simulation
Journal of Computational and Applied Mathematics
Stability analysis of uncertain neural networks with linear and nonlinear time delays
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Stability analysis of neutral neural networks with time delay
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
IEEE Transactions on Neural Networks
Multiperiodicity of Discrete-Time Delayed Neural Networks Evoked by Periodic External Inputs
IEEE Transactions on Neural Networks
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
New results for robust stability of dynamical neural networks with discrete time delays
Expert Systems with Applications: An International Journal
Mathematics and Computers in Simulation
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In this paper, we have investigated the existence and uniqueness and the global exponential stability of the equilibrium point for Cohen-Grossberg type BAM neural networks with time-varying delays and continuously distributed delays. Based on Lyapunov stability theory, some sufficient conditions ensuring existence and uniqueness of the equilibrium point and the global exponential stability are derived. These new conditions obtained here include some previously known criteria. Finally, an illustrative example is given to demonstrate the effectiveness of our obtained results.