Finite-Time Synchronization of Stochastic Reaction Diffusion Neural Networks under Complex Noise

This research focuses on the finite-time synchronization (FTS) problem of stochastic reaction diffusion neural networks (SRDNNs) under complex noise. Firstly, we introduce Levy noise as a disturbance to the system, thereby enhancing its applicability to practical engineering scenarios. Subsequently, a nonlinear feedback controller is designed in the study, which successfully achieves FTS for master-slave SRDNNs. Moreover, utilizing Lyapunov stability theory, the study derives sufficient conditions and criteria for achieving FTS in SRDNNs by constructing an appropriate Lyapunov functional and incorporating the Ito formula. Finally, through a numerical simulation experiment, the validity and practicality of the theoretical research findings are verified.