School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
Neural Netw. 2021 Jul;139:255-264. doi: 10.1016/j.neunet.2021.02.029. Epub 2021 Mar 22.
This paper considers the drive-response synchronization of memristive neural networks (MNNs) with unknown parameters, where the unbounded discrete and bounded distributed time-varying delays are involved. Aiming at the unknown parameters of MNNs, the updating law of weight in response system and the gain of adaptive controller are proposed to realize the synchronization of delayed MNNs. In view of the limited communication and bandwidth, the event-triggered mechanism is introduced to adaptive control, which not only decreases the times of controller update and the amount of data sending out but also enables synchronization when parameters of MNNs are unknown. In addition, a relative threshold strategy, which is relative to fixed threshold strategy, is proposed to increase the inter-execution intervals and to improve the control effect. When the parameters of MNNs are known, the algebraic criteria of synchronization are established via event-triggered state feedback control by exploiting inequality techniques and calculus theorems. Finally, one simulation is presented to validate the effectiveness of the proposed results.
本文考虑了具有未知参数的忆阻神经网络（MNNs）的驱动-响应同步问题，其中涉及无界离散和有界分布时变延迟。针对 MNNs 的未知参数，提出了响应系统中权值的更新律和自适应控制器的增益，以实现延迟 MNNs 的同步。针对通信和带宽有限的问题，将事件触发机制引入自适应控制中，不仅减少了控制器更新的次数和数据发送量，而且在 MNNs 参数未知时也能实现同步。此外，提出了一种相对阈值策略，与固定阈值策略相比，该策略增加了执行间隔，提高了控制效果。当 MNNs 的参数已知时，通过利用不等式技术和微积分定理的事件触发状态反馈控制，建立了同步的代数判据。最后，通过一个仿真验证了所提出结果的有效性。
