Event-triggered distributed control for synchronization of multiple memristive neural networks under cyber-physical attacks

Abstract

This paper investigates the synchronization of multiple memristive neural networks (MMNNs) under cyber-physical attacks through distributed event-triggered control. In the field of multi-agent dynamics, memristive neural network (MNN) is considered as a kind of switched systems because of its state-dependent parameters which can lead to the parameters mismatch during synchronization. This will increase the uncertainty of the system and affect the theoretical analysis. Also, neural network is considered as a typical nonlinear system. Therefore, the model studied in this paper is a nonlinear system with switching characteristics. In complex environments, MMNNs may receive mixed attacks, one of which is called cyber-physical attacks that may influence both communication links and MNN nodes to cause changes in topology and physical state. To tackle this issue, we construct a novel Lyapunov functional and use properties of M-matrix to get the criteria for synchronization of MMNNs under cyber-physical attacks. It is worth mentioning that the controllers in this paper are designed to be distributed under event-triggering conditions and Zeno behavior is also excluded. In addition, the algorithm of parameter selection is given to help designing the controllers. One example is given at the end of the paper to support our results.