This paper concerns the fault detection filtering problem for discrete-time memristive neural networks with mixed time delays. An improved dynamic event-triggering protocol, whose multiple threshold functions are dynamically adjustable, is presented to decrease the utilization of limited resources and achieve desired performance. Two mutually independent Bernoulli variables are given to depicting the randomly occurring cyber-attacks. Meanwhile, a dynamic quantizer is established to account for restricted bandwidth efficiently. Based on the Lyapunov theory, sufficient conditions are derived to ensure the filtering error system is exponential mean square stable and desired performance. In the end, a numerical example is provided to verify the effectiveness of the proposed methodology.

本文研究了具有混合时延的离散时间忆阻神经网络的故障检测滤波问题。提出了一种改进的动态事件触发协议，它的多个阈值函数是动态可调的，以减少有限资源的利用率并达到预期的性能。给出了两个相互独立的伯努利变量来描述随机发生的网络攻击。同时，建立动态量化器以有效地解决受限带宽问题。基于李雅普诺夫理论，推导了保证滤波误差系统指数均方稳定和期望性能的充分条件。最后，给出了一个数值例子来验证所提方法的有效性。