Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
BAM-type Cohen-Grossberg neural networks with time delays
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
Global Exponential Stability of Multitime Scale Competitive Neural Networks With Nonsmooth Functions
IEEE Transactions on Neural Networks
A one-parameter filled function method applied to nonsmooth constrained global optimization
Computers & Mathematics with Applications
IEEE Transactions on Neural Networks
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Dynamics of competitive neural networks with inverse lipschitz neuron activations
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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In this paper, by using nonsmooth analysis approach, linear matrix inequality (LMI) technique, topological degree theory and Lyapunov-Krasovskii function method, the issue of global exponential stability is investigated for a class of generalized Cohen-Grossberg neural networks possessing inverse Lipschitz neuron activations and nonsmooth behaved functions. Several novel delay-dependent sufficient conditions are established towards the existence, uniqueness and global exponential stability of the equilibrium point, which are shown in terms of LMIs. It is noted that the results above require neither the Lipschitz continuity of the activation functions, nor the smoothness of the behaved functions. Also, for the case of the activation function that satisfies not only the inverse Lipschitz conditions but also the Lipschitz conditions, some conditions are derived which generalize the previous results. Finally, two examples with their simulations are given to show the effectiveness of the theoretical results.