Global Exponential Convergence of Cohen–Grossberg Neural Networks With Time Delays
Hongtao Lu, R. Shen, Fu-Lai Chung · IEEE Transactions on Neural Networks · 2005
In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.