Impulsive Effects on Stability of Fuzzy Cohen–Grossberg Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Circuits and Systems II: Express Briefs
Improved robust stability criteria for delayed cellular neural networks via the LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
An augmented LKF approach involving derivative information of both state and delay
IEEE Transactions on Neural Networks
Mathematics and Computers in Simulation
Novel stability criteria of Cohen–Grossberg neural networks with time-varying delays
International Journal of Circuit Theory and Applications
LMI-Based lagrange stability of CGNNs with general activation functions and mixed delays
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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In this paper, the problem on exponential stability analysis of recurrent neural networks with both time-varying delays and general activation functions is considered. Neither the boundedness and the monotony on these activation functions nor the differentiability on the time-varying delays are assumed. By employing Lyapunov functional and the free-weighting matrix method, several sufficient conditions in linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed stability results are less conservative than some recently known ones in the literature, which is demonstrated via an example with simulation.