Exponential Stability of Stochastic Time-Delay Neural Networks with Random Delayed Impulses

• DOI: 10.1007/s11063-024-11521-3
• Publication Date: 2024-02-15T15:02:56Z
• Authors: Yueli Huang; Ailong Wu; Jin-E Zhang
• Keywords: Retard

Abstract The mean square exponential stability of stochastic time-delay neural networks (STDNNs) with random delayed impulses (RDIs) is addressed in this paper. Focusing on the variable delays impulses, notion average delay adopted to consider these as a whole, and criterion STDNNs RDIs developed by using analysis idea Lyapunov method. Taking into account impulsive effect, interference function stabilization are explored independently. results demonstrate that properties take crucial role dynamics STDNNs, not only making stable unstable, but also stabilizing unstable STDNNs. Our conclusions, specifically, allow for both continuous subsystems surpass length interval, which alleviates certain severe limitations, such presence upper bound or requirement can exist between two events. Finally, feasibility theoretical verified through three simulation examples.