LMI-based criteria for globally robust stability of delayed Cohen-Grossberg neural networks

The issue of globally robust asymptotic stability with norm-bounded parameter uncertainties is studied for delayed Cohen-Grossberg neural networks. By constructing a suitable Lyapunov functional, several sufficient conditions are obtained guaranteeing the global robust convergence of the equilibrium point. The obtained conditions are given in the form of matrix and linear matrix inequalities that can be checked numerically and very efficiently by resorting to the recently developed interior-point method. Finally, an illustrative numerical example is provided to demonstrate the effectiveness of the obtained results.