School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo 454000, China.
Department of Artificial Intelligence and Data Science, Guangzhou Xinhua University, Guangzhou 523133, Guangdong Province, China.
Comput Intell Neurosci. 2022 Sep 27;2022:8157794. doi: 10.1155/2022/8157794. eCollection 2022.
In this paper, the globally asymptotic synchronization of multi-layer neural networks is studied via aperiodically intermittent control. Due to the property of intermittent control, it is very hard to deal with the effect of time-varying delays and ascertain the control and rest widths for intermittent control. A new lemma with generalized Halanay-type inequalities are proposed first. Then, by constructing a new Lyapunov-Krasovskii functional and utilizing linear programming methods, several useful criteria are derived to ensure the multilayer neural networks achieve asymptotic synchronization. Moreover, an aperiodically intermittent control is designed, which has no direct relationship with control widths and rest widths and extends existing aperiodically intermittent control techniques, the control gains are designed by solving the linear programming. Finally, a numerical example is provided to confirm the effectiveness of the proposed theoretical results.
本文通过非周期间断控制研究了多层神经网络的全局渐近同步。由于间断控制的性质，很难处理时变时滞的影响，并确定间断控制的控制和休息宽度。首先提出了具有广义 Halanay 型不等式的新引理。然后，通过构造新的 Lyapunov-Krasovskii 泛函并利用线性规划方法，得出了几个有用的判据，以确保多层神经网络实现渐近同步。此外，设计了一种非周期间断控制，它与控制宽度和休息宽度没有直接关系，并扩展了现有的非周期间断控制技术，控制增益通过求解线性规划来设计。最后，通过一个数值例子验证了所提出的理论结果的有效性。
