Adaptive PD-Like Event-Triggered Secure Synchronization Control for Inertial Neural Networks and Signal Encryption Application
研究了马尔可夫跳变惯性神经网络在混合攻击下的指数安全同步问题，提出一种自适应类比例微分事件触发机制，用于信号加密，并通过数值和音频加密示例验证了有效性。
This article investigates the exponential secure synchronization problem of Markovian jumping delayed inertial neural networks (INNs) under hybrid attacks, which is applied in signal encryption. The novel adaptive proportional-derivative (PD)-like event-triggered mechanism (APDETM), including the variational tendency of states, is proposed by adopting both proportional and derivative terms, aiming to further filter out redundant sampling data while preserving effective system performance. In addition, the INNs with generally uncertain semi-Markovian (GUSM) jumping parameters are established under denial-of-service (DoS) and deception attacks (DAs). Meanwhile, the event-triggered output feedback controllers are designed to achieve the secure synchronization control of the drive and response systems subject to hybrid attacks. Based on the chaotic behaviors of the INNs, the event-triggered synchronization conditions are applied to the signal encryption field. Finally, two examples, including a numerical simulation and an audio encryption process, are shown to demonstrate the effectiveness of the proposed methods.