Reachable Set Estimation for a Class of Memristor-Based Neural Networks With Time-Varying Delays
Jiemei Zhao · IEEE Access · 2017
This paper investigates the reachable set estimation problem for a class of memristor-based neural networks with time-varying delays and bounded disturbances. By constructing a Lyapunov- Krasovskii functional, a sufficient condition for the solvability of the addressed problem is established based on linear matrix inequality. This condition ensuring the existence of an ellipsoid that contains all the states under initial conditions. A stability criterion of memristor-based neural networks with timevarying delays is also given. Two numerical examples are provided to show the effectiveness of the proposed methods.