Dissipative control for quaternion-valued fuzzy memristive neural networks: Nonlinear scalarization approach

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

Fuzzy Sets and Systems Pub Date : 2024-05-03 DOI:10.1016/j.fss.2024.108996

Hongzhi Wei , Hongjun Zhou , Ruoxia Li , Ning Li

引用次数: 0

Abstract

This paper deals with the dissipative control for a class of quaternion-valued fuzzy memristive neural networks. By constructing proper Lyapunov functionals and using adaptive controller, the strictly $(Q, S, R)$ -dissipative are characterized parametrically. Then, based on the algebraic inequality and linear matrix inequality (LMI) approach, sufficient conditions for the existence of the dissipative controllers are obtained. In addition, the nonlinear scalarization approach is developed, which can be employed to compare the “size” of two different quaternions, in this way, the convex closure proposed by the quaternion weights are meaningful. Finally, simulation examples are given to show the efficiency of the proposed methods.

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四元值模糊记忆神经网络的耗散控制：非线性标量化方法

本文论述了一类四元值模糊记忆神经网络的耗散控制。通过构建适当的 Lyapunov 函数和使用自适应控制器，对严格（Q,S,R）耗散进行了参数化描述。然后，基于代数不等式和线性矩阵不等式（LMI）方法，得到了耗散控制器存在的充分条件。此外，还开发了非线性标量化方法，可用于比较两个不同四元数的 "大小"，这样，四元数权重提出的凸封闭就有意义了。最后，还给出了模拟示例，以显示所提方法的效率。

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

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.