Wang Xiangxiang, Yu Yongbin, Cai Jingye, Zhong Shouming, Yang Nijing, Shi Kaibo, Adu Kwabena, Tashi Nyima
IEEE Trans Neural Netw Learn Syst. 2023 Jul;34(7):3501-3515. doi: 10.1109/TNNLS.2021.3112068. Epub 2023 Jul 6.
This article investigates the problem of relaxed exponential stabilization for coupled memristive neural networks (CMNNs) with connection fault and multiple delays via an optimized elastic event-triggered mechanism (OEEM). The connection fault of the two or some nodes can result in the connection fault of other nodes and cause iterative faults in the CMNNs. Therefore, the method of backup resources is considered to improve the fault-tolerant capability and survivability of the CMNNs. In order to improve the robustness of the event-triggered mechanism and enhance the ability of the event-triggered mechanism to process noise signals, the time-varying bounded noise threshold matrices, time-varying decreased exponential threshold functions, and adaptive functions are simultaneously introduced to design the OEEM. In addition, the appropriate Lyapunov-Krasovskii functionals (LKFs) with some improved delay-product-type terms are constructed, and the relaxed exponential stabilization and globally uniformly ultimately bounded (GUUB) conditions are derived for the CMNNs with connection fault and multiple delays by means of some inequality processing techniques. Finally, two numerical examples are provided to illustrate the effectiveness of the results.
本文通过一种优化的弹性事件触发机制（OEEM）研究了具有连接故障和多个时滞的耦合忆阻神经网络（CMNNs）的松弛指数镇定问题。两个或某些节点的连接故障会导致其他节点的连接故障，并在CMNNs中引发迭代故障。因此，考虑采用备用资源的方法来提高CMNNs的容错能力和生存能力。为了提高事件触发机制的鲁棒性，增强事件触发机制处理噪声信号的能力，同时引入时变有界噪声阈值矩阵、时变递减指数阈值函数和自适应函数来设计OEEM。此外，构造了具有一些改进的延迟积分类项的适当的Lyapunov-Krasovskii泛函（LKFs），并通过一些不等式处理技术推导了具有连接故障和多个时滞的CMNNs的松弛指数镇定和全局一致最终有界（GUUB）条件。最后，给出了两个数值例子来说明结果的有效性。
