具有时变延迟的 T-S 模糊马尔可夫跃迁奇异系统的改进可接受性分析

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Fuzzy Sets and Systems Pub Date : 2024-07-02 DOI:10.1016/j.fss.2024.109069
Xingyue Liang, Shengyuan Xu
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引用次数: 0

摘要

本文研究了具有时变延迟的 T-S 模糊马尔可夫跃迁奇异系统(MJSS)的可接受性分析问题。首先,基于新的状态分解向量,提出了一个扩展的 Lyapunov-Krasovskii 函数(LKF),该函数允许所涉及的矩阵并非都是正定矩阵。而且,这个 LKF 不仅考虑了状态积分变量,还充分利用了成员函数的时间导数信息,这无疑为结果带来了优势。然后,基于自由矩阵积分不等式,推导出一个修正版本,以估计 LKF 推导过程中产生的积分项的值。基于扩展的 LKF 和修正的积分不等式,得到了 T-S 模糊 MJSS 的较少保守的可接受性准则。最后,通过两个仿真实例证明了所提方法的有效性。
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Improved admissibility analysis for T-S fuzzy Markovian jump singular systems with time-varying delays

This article researches the admissibility analysis problem for T-S fuzzy Markovian jump singular systems (MJSSs) with time-varying delays. First, based on the new state decomposition vectors, an extended Lyapunov-Krasovskii functional (LKF) is formulated, which allows that the involved matrices are not all positive definite. And, this LKF not only considers the state integral variable, but also makes full use of the time derivative information of membership function, which undoubtedly provides an advantage to the results. Then, based on free matrix integral inequality, a modified version is derived to estimate the value of integral term generated during the derivation of LKF. Based on extended LKF and modified integral inequality, the admissibility criterion with less conservative is obtained for T-S fuzzy MJSSs. Finally, two simulation examples are offered to demonstrate the effectiveness of proposed method.

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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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