Fault-tolerant and attack-tolerant cooperative event-triggered sampled-data security control for synchronization of RDNNs with stochastic actuator failures and random deception attacks

In this article, the fault-tolerant and attack-tolerant cooperative event-triggered sampled-data security (FACETSDS) synchronization problem of space-varying reaction–diffusion neural networks (SVRDNNs) under spatially point measurements (SPMs) with stochastic actuator failures and random deception attacks is investigated. First, to save more communication resources and adapt to the variation of system dynamics subject to stochastic actuator failures and random deception attacks, a FACETSDS control scheme is proposed under SPMs. Second, by constructing a Lyapunov functional and utilizing inequality techniques, some synchronization criteria based on spatial linear matrix inequalities (SLMIs) are derived for SVRDNNs. Then, to solve SLMIs, the FETSDS control for synchronization problem of SVRDNNs under SPMs with stochastic actuator failures and random deception attacks is formulated as an linear matrix inequality feasibility problem. Lastly, the designed FACETSDS synchronization strategy is verified by one numerical example.