Non‐fragile set‐membership estimation for sensor‐saturated memristive neural networks via weighted try‐once‐discard protocol

Abstract

This study is concerned with the non‐fragile set‐membership estimation problem for a class of delayed sensor‐saturated memristive neural networks under the premise of communication protocol transmissions. The exogenous noises are unknown but bounded and the activation function satisfies the sector‐bounded condition. In order to schedule the limited network resources, the node's utilisation right of the communication channel at the current instant is determined by the weighted try‐once‐discard protocol. Besides, the estimator gain perturbations are considered to enhance the robustness of estimation method. The major focus of the study is to a design a non‐fragile estimator such that, for all measurement saturation, mixed time‐delays and estimator gain perturbation, the estimation error exists in the ellipsoid by providing a sufficient criterion and the optimal semi‐axes length of the ellipsoid is found by solving a convex optimisation problem. Finally, a simulation example shows that the proposed non‐fragile estimation strategy is effective.