【摘要】 本文聚焦一类饱和忆阻神经网络模型，利用攻击驱动的事件触发控制策略，研究了其在概率欺骗攻击下的均方稳定问题。首先，针对器件物理限制或设备损耗导致的执行器饱和现象，本文建立了饱和忆阻神经网络模型，解决了执行器与控制器间的信号传输存在较大偏差造成的控制失效问题。其次，网络的强开放性和不可预测性使信号在传输时容易受到攻击，本文结合伯努利过程设计了具有时变时延的概率欺骗攻击模型和攻击驱动的事件触发控制策略，提升了系统应对复杂环境的抗干扰能力，并且减少了数据传输量，节省了通信资源。此外，本文构建了一种依赖于事件触发时刻的新型李雅普诺夫泛函，加强了对系统状态信息的利用，并通过李雅普诺夫稳定性理论和不等式技术，推导出系统均方稳定的充分条件。最后，通过数值仿真验证了结果的可行性和有效性。更多还原

【Abstract】 This paper focuses on a class of saturated memristive neural network models and investigates its mean square stable problem under probabilistic deception attacks with an attack-driven event-triggered control strategy. First, aiming at the actuator saturation phenomenon under device physical limitations or equipment loss, this paper establishes the saturated memristive neural networks, which resolve the issue of control failure caused by significant deviations in signal transmission between actuators and controllers. Next, the strong openness and unpredictability of the network make signals susceptible to attacks during transmission, this paper combines Bernoulli processes to design a probabilistic deception attack model with time-varying delays and an attack-driven event-triggered control strategy, which enhances the anti-interference ability of the system in complex environments, reduces data transmission, and conserves communication resources. Furthermore, this paper constructs a novel Lyapunov functional dependent on event-triggered instants, reinforcing the utilization of system state information. Then,through Lyapunov stability theory and inequality techniques, sufficient conditions for the mean square stability of the system are derived. Ultimately, the feasibility and effectiveness of the results are verified through numerical examples.更多还原