基于阶跃函数法的T-S模糊部分耦合复杂网络的稳定性分析

IF 3.2 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2025-02-19 DOI:10.1016/j.fss.2025.109318

Shiju Yang , Tingting Huang , Dongmei Ruan , Hongsen He

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引用次数: 0

摘要

本文主要讨论了带钉住脉冲控制的T-S模糊部分耦合复杂网络（T-S FPCCNs）的稳定性。与传统的脉冲控制方法不同，采用阶跃函数法将稳定性分析过程分为两部分（第一部分是单跨阶跃函数，第二部分是多跨阶跃函数）。然后分析了具有钉住脉冲控制的T-S模糊部分耦合复杂网络的稳定性。此外，还可以设计新颖的钉住脉冲控制器，保证网络达到全局均匀吸引稳定（GUAS）。利用重组方法和脉冲控制理论构造了一个比较系统，给出了保证T-S fpccn稳定性的几个新的充分判据。最后，通过实验实例验证了理论分析的正确性。

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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Stability analysis of T-S fuzzy partially coupled complex networks with pinning impulsive controllers by step function method

This paper mainly discusses the stability of T-S fuzzy partially coupled complex networks (T-S FPCCNs) with pinning impulsive control. Different from the traditional impulsive control method, the analysis process of the stability can be divided into two parts by adopting the step-function method (the first part is one-span step-function and the second part is multi-span step-function). Then the stability of T-S fuzzy partially coupled complex networks with pinning impulsive control can be analyzed. In addition, the novel pinning impulsive controllers can be designed to ensure the network to achieve globally uniformly attractively stable (GUAS). Furthermore, a comparison system is constructed by using regrouping method and impulsive control theory, and several new sufficient criterions are given to ensure the stability of the T-S FPCCNs. Finally, the correctness of theoretical analysis is proved by experimental cases.

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来源期刊

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.