Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Impulsive effects on stability of Cohen-Grossberg neural networks with variable delays
Applied Mathematics and Computation
Delay-dependent stability analysis for impulsive BAM neural networks with time-varying delays
Computers & Mathematics with Applications
Stability of fuzzy cellular neural networks with impulses
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Delay-independent stability in bidirectional associative memory networks
IEEE Transactions on Neural Networks
Mathematics and Computers in Simulation
IEEE Transactions on Neural Networks
Journal of Computational and Applied Mathematics
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In this paper, a class of Cohen-Grossberg-type BAM neural networks with time-varying delays are studied. Some sufficient conditions are established for the existence, uniqueness and exponential stability of the equilibrium point by using Lyapunov functionals, the analysis method and impulsive control. Here we point out that our result, which is different from previous known results, shows that the unstable Cohen-Grossberg-type BAM neural networks with time-varying delays can be exponentially stabilized via impulsive control. Moveover, the estimate of the exponential convergence rate is also obtained, which depends on the system parameters. Finally, an illustrative example is given to show the effectiveness of the proposed method and result.