Global exponential stability of a class of neural networks with variable delays
Computers & Mathematics with Applications
Exponential p-stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays
Mathematics and Computers in Simulation
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In this paper, the Cohen-Grossberg neural networks with time delays are considered without assuming any symmetry of connection matrix and differentiability of the activation functions. By constructing a novel Lyapunov functional, new sufficient criteria for the existence of a unique equilibrium and global exponential stability of the network are derived. These criteria are all independent of the magnitudes of delays, and so the delays under these conditions are harmless. Those results are shown to generalize the previous global exponential stability results derived in the literature.