Input-to-state stability of memristor-based complex-valued neural networks with time delays

This paper concentrates on the input-to-state stability problem for a class of memristor-based complex-valued neural networks with time delays. Different from the input-to-state stability criteria of real-valued neural networks, several new stability criteria of complex-valued neural networks are proposed by utilizing the Lyapunov function method, the differential inclusions theory and set-valued maps. The obtained results generalize some existing literature about real-valued neural networks as special conditions. A numerical example is presented to demonstrate the effectiveness of our theoretical results.