Exponential H∞ State Estimation for Memristive Neural Networks: Vector Optimization Approach

This article presents the theoretical results on the
$H_\infty $
state estimation problem for a class of discrete-time memristive neural networks. By utilizing a Lyapunov–Krasovskii functional, sufficient conditions are derived to guarantee that the error system is exponentially mean-square stable; subsequently, the prespecified
$H_\infty $
disturbance rejection attenuation level is also guaranteed. It should be noted that the vector optimization method is employed to find the maximum bound of function and the minimum disturbance turning simultaneously. Finally, the corresponding simulation results are included to show the effectiveness of the proposed methodology.