Output synchronization in fixed/preassigned-time of T-S fuzzy multilayered networks

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Fuzzy Sets and Systems Pub Date : 2025-01-16 DOI:10.1016/j.fss.2025.109279
Yuhua Gao , Cheng Hu , Juan Yu
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Abstract

Considering the function diversity of individuals in the actual system, the uncertainty and fuzziness in modeling network dynamics, a class of T-S fuzzy multilayered networks is concerned in this article, and the fixed/preassigned-time output synchronization is explored to overcome the immeasurability of node states. Firstly, to remove the connectivity restriction of network topology, the synchronous state, which can be any specified smooth orbit, is added to the original network as a virtual individual. Subsequently, a type of continuous fuzzy control law is developed to realize fixed/preassigned-time output synchronization for multilayered networks, and several synchronization criteria are gained based on fixed-time stability theory and inequality technique. Notice that the link between the estimate of settling-time and the number of network layers is revealed, and the restriction on the output matrix is largely weakened compared to some previous researches. Lastly, the proposed fuzzy control schemes and output synchronization criteria are verified by numerical simulations and are applied to signal security communication, in which the robustness and sensitivity of the developed control method are analyzed.
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考虑到实际系统中个体功能的多样性、网络动力学建模的不确定性和模糊性,本文关注一类 T-S 模糊多层网络,并探索了固定/预分配时间输出同步来克服节点状态的不可测性。首先,为了消除网络拓扑结构的连通性限制,将同步状态作为虚拟个体添加到原始网络中,同步状态可以是任意指定的平滑轨道。随后,建立了一种连续模糊控制法则来实现多层网络的固定/预分配时间输出同步,并基于固定时间稳定性理论和不等式技术获得了若干同步准则。与之前的一些研究相比,本文揭示了沉降时间估计值与网络层数之间的联系,并在很大程度上弱化了对输出矩阵的限制。最后,通过数值模拟验证了所提出的模糊控制方案和输出同步准则,并将其应用于信号安全通信中,分析了所开发控制方法的鲁棒性和灵敏度。
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来源期刊
Fuzzy Sets and Systems
Fuzzy Sets and Systems 数学-计算机:理论方法
CiteScore
6.50
自引率
17.90%
发文量
321
审稿时长
6.1 months
期刊介绍: Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.
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