Kazemy Ali, Lam James, Zhang Xian-Ming
IEEE Trans Neural Netw Learn Syst. 2022 Mar;33(3):952-961. doi: 10.1109/TNNLS.2020.3030638. Epub 2022 Feb 28.
The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic deception attacks modeled by two independent Markov processes. Two discrete event-triggered mechanisms are introduced for both channels to reduce the number of data transmission through the communication channels. To comply with practical point of view, static output feedback is utilized. By employing the Lyapunov-Krasovskii functional method, some sufficient conditions on the synchronization of master-slave neural networks are derived in terms of linear matrix inequalities, which make it easy to design suitable output feedback controllers. Finally, a numerical example is presented to show the effectiveness of the proposed method.
本文研究了主从神经网络的事件触发同步问题。假设从传感器到控制器以及从控制器到执行器的通信通道都受到由两个独立马尔可夫过程建模的随机欺骗攻击。针对这两个通道引入了两种离散事件触发机制，以减少通过通信通道的数据传输次数。从实际角度出发，采用静态输出反馈。通过运用李雅普诺夫 - 克拉索夫斯基泛函方法，根据线性矩阵不等式得出了主从神经网络同步的一些充分条件，这使得设计合适的输出反馈控制器变得容易。最后，给出了一个数值例子以说明所提方法的有效性。
