Quantized control for a class of neural networks with adaptive event‐triggered scheme and complex cyber‐attacks

This article is concerned with the quantized control problem for neural networks with adaptive event‐triggered scheme (AETS) and complex cyber‐attacks. By fully considering the characteristics of cyber‐attacks, a mathematical model of complex cyber‐attacks, which consists of replay attacks, deception attacks, and denial‐of‐service (DoS) attacks, is firstly built for neural networks. For the sake of relieving the pressure under limited communication resources, an AETS and a quantization mechanism are employed in this article. By utilizing Lyapunov stability theory, adequate conditions ensuring the stability of neural networks are obtained. Moreover, the controller gain is derived by solving a set of linear matrix inequalities. At last, the usefulness of the proposed method is verified by a numerical example.