Reachable set performance and quantized sampled-data synchronization analysis of neural networks under random packet dropouts via enhanced looped functional

Abstract

The study aims to present the synchronization concerns of neural networks with stochastic packet dropouts utilizing a quantized memory sampled-data control (QMSDC) scheme and the problem of reachable set analysis (RSA) for NNs under the influence of external disturbances are discussed. To do this, a novel free-weighting matrix integral inequality (FWMII) is proposed with an augmented Lyapunov-functional. Furthermore, at this time, we show that the proposed inequality is less conservative due to the delay information. In the meantime, a novel enhanced looped function is created that utilizes not only sampling information but also incorporates both transmission delay information and a quantized sampling pattern. Next, using the proposed FWMII technique, several sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to suggest how an augmented closed-loop system can achieves stochastic mean-square exponential synchronization under random packet loss situations. Finally, the proposed approach demonstrates its usefulness and superiority by comparing it with existing methods through numerical simulation of the secure communications.