Stochastic Sampled-Data Security Synchronization Control for Delayed Reaction–Diffusion Neural Networks Under Multisource Deception Attacks

In this article, stochastic sampled-data (SD) security synchronization control is investigated for delayed reaction–diffusion neural networks (RDNNs) subject to multisource deception attacks under spatially point measurements (SPMs). The SD security synchronization scheme, which takes into account the communication limitations of multisource deception attacks and stochastic sampling, is based on SPMs and is available only at a finite number of fixed spatial points. On the basis of the Lyapunov functional (LF) and inequality techniques, the mean square exponential stability of the synchronization error system (SES) is ensured by synchronization criteria established using a stochastic SD security controller under SPMs and expressed as linear matrix inequalities (LMIs). Finally, two examples are provided to demonstrate the effectiveness of the presented stochastic SD security synchronization control scheme.