Identification for Weighted Matrices of Delayed Multilayer Stochastic Complex Network With Lévy Noise

IF 5.3 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-06-23 DOI:10.1109/TCNS.2026.3706636

Chunmei Zhang;Jiamin Zhou;Yanlong Zhao

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引用次数: 0

Abstract

In this article, the novel identification criteria for weighted matrices (WMs) of a delayed multilayer stochastic complex network with Lévy noise (DMSCNL) are investigated based on adaptive synchronization. Compared with previous works, this is the first study on the problem of identification for WMs of a stochastic complex network with Lévy noise. Specifically, considering DMSCNL with unknown WMs as the original system, an auxiliary system with an adaptive feedback controller and estimated weighted values is designed to track the original system by combining graph theory with the nonnegative semimartingale convergence theorem. Furthermore, the estimated weighted values in the auxiliary system can converge to the unknown weighted values in the original system under an excitation condition. That is, the corresponding identification criteria for WMs of the original system are derived. Moreover, theoretical results are validated through a two-layer recurrent neural network. Finally, numerical simulations well verify the validity of theoretical results.

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具有lsamvy噪声的延迟多层随机复杂网络的加权矩阵辨识

研究了一种基于自适应同步的延迟多层随机杂波网络加权矩阵辨识准则。与以往的工作相比，本文首次研究了随机复杂网络中含有lsamvy噪声的WMs识别问题。具体而言，以未知WMs的DMSCNL为原系统，结合图论和非负半鞅收敛定理，设计了一个带有自适应反馈控制器和估计权值的辅助系统来跟踪原系统。在一定的激励条件下，辅助系统中的估计权值可以收敛到原系统中的未知权值。也就是说，对原系统的WMs给出了相应的识别准则。通过双层递归神经网络对理论结果进行了验证。最后，通过数值模拟很好地验证了理论结果的有效性。

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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来源期刊

IEEE Transactions on Control of Network Systems Mathematics-Control and Optimization

CiteScore

7.80

自引率

7.10%

发文量

169

期刊介绍： The IEEE Transactions on Control of Network Systems is committed to the timely publication of high-impact papers at the intersection of control systems and network science. In particular, the journal addresses research on the analysis, design and implementation of networked control systems, as well as control over networks. Relevant work includes the full spectrum from basic research on control systems to the design of engineering solutions for automatic control of, and over, networks. The topics covered by this journal include: Coordinated control and estimation over networks, Control and computation over sensor networks, Control under communication constraints, Control and performance analysis issues that arise in the dynamics of networks used in application areas such as communications, computers, transportation, manufacturing, Web ranking and aggregation, social networks, biology, power systems, economics, Synchronization of activities across a controlled network, Stability analysis of controlled networks, Analysis of networks as hybrid dynamical systems.