基于事件触发控制的时变时滞神经网络主从同步

IF 1.8 4区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2020-06-16 DOI:10.1080/13873954.2020.1777567

Jun Zhou, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou

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引用次数: 4

摘要

摘要本文通过事件触发控制（ETC）研究了具有时变时滞的神经网络的主从同步问题。首先，所提出的ETC可以有效地减少同步过程中传输到控制器的数据总量，避免通信信道拥塞。其次，构造了具有时变时滞的神经网络的主从同步，其中神经网络内的时滞和ETC同时存在。利用李雅普诺夫稳定性理论，得到了保证神经网络主从同步的充分准则。最后，通过数值算例和隧道二极管电路算例验证了所得结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Master-slave synchronization of neural networks with time-varying delays via the event-triggered control

ABSTRACT This paper investigates the problem of master-slave synchronization of neural networks with time-varying delays via the event-triggered control (ETC). First, the proposed ETC can effectively reduce the total amount of data transmitted to the controller in the synchronization process and avoid communication channel congestion. Second, a master-slave synchronization of neural networks with time-varying delays is constructed, where delays within neural networks and the ETC are simultaneous existence. The controller is updated by the ETC. By the Lyapunov stability theory, some sufficient criteria are obtained to ensure master-slave synchronization of neural networks. Finally, a numerical example and a tunnel diode circuit example are used to verify the validity of results obtained.

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来源期刊

Mathematical and Computer Modelling of Dynamical Systems 数学-计算机：跨学科应用

CiteScore

3.80

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.