基于观测器的拓扑识别和有限时间内通过动态事件触发控制实现分数奇异扰动复杂网络的同步

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lingyan Wang, Huaiqin Wu, Jinde Cao
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引用次数: 0

摘要

本文研究了分数奇异扰动复杂网络(FSPCN)的拓扑识别和有限时间内的同步问题。首先,本文提出了连续微分函数的收敛原理。其次,设计了一个动态事件触发机制(DETM)来实现网络同步,并开发了一个拓扑观测器来识别网络拓扑。第三,在设计的 DETM 下,通过构建 Lyapunov 函数和应用不等式分析技术,以矩阵不等式的形式确定了有限时间内的拓扑识别和同步条件。此外,还证明了可以有效地排除 Zeno 行为。最后,通过一个应用实例验证了主要结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

This paper investigates the topology identification and synchronization in finite time for fractional singularly perturbed complex networks (FSPCNs). Firstly, a convergence principle is developed for continuously differential functions. Secondly, a dynamic event-triggered mechanism (DETM) is designed to achieve the network synchronization, and a topology observer is developed to identify the network topology. Thirdly, under the designed DETM, by constructing a Lyapunov functional and applying the inequality analysis technique, the topology identification and synchronization condition in finite time is established in the forms of the matrix inequality. In addition, it is proved that the Zeno behavior can be effectively excluded. Finally, the effectiveness of the main results is verified by an application example.

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来源期刊
Neural Processing Letters
Neural Processing Letters 工程技术-计算机:人工智能
CiteScore
4.90
自引率
12.90%
发文量
392
审稿时长
2.8 months
期刊介绍: Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
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