On Robust Stabilization of A Class of Neural Networks with Time-Varying Delays

This paper deals with the stabilization problem for a class of delayed neural networks, which covers the Hopfield neural networks and cellular neural networks with time-varying delays. A feedback control gain matrix is derived to achieve the exponential stabilization of the neural networks by using the Lyapunov stability theory, and the stabilization condition can be verified if a certain Hamiltonian matrix with no eigenvalues on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. The results make a preparation for the research about stabilization of delayed neural networks and further the earlier researches. A numerical example illustrates the effectiveness of the results

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