线性相关空间中的模糊变分微积分:第1部分

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Gastão S.F. Frederico , Estevão Esmi , Laécio C. Barros
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引用次数: 0

摘要

本文是一系列文章的第一部分,旨在介绍在线性相关模糊数RF(A)空间中取值的函数的模糊变分演算的基础。回想一下,空间RF(A)由实数r和模糊数qA的所有和组成,其中A是给定的非对称模糊数,q是任意实数。该空间的两个优点是,它可以配备巴拿赫空间结构,并且与统计学中的加性模型类似,其元素可以解释为确定性预期/可预测值与不确定/噪声分量的总和。变分微积分理论的建立需要定义和建立许多概念和结果。本文给出了RF(a)上的一个全序关系,并由此导出了RF(a)值函数f的局部极小值和局部极大值的概念。最后,我们给出了变分微积分理论中必不可少的du Bois-Reymond引理的一个广义模糊版本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy variational calculus in linearly correlated space: Part I
This article is the first part of series of articles that aim to present the foundations for fuzzy variational calculus for functions taking values in the space of linearly correlated fuzzy numbers RF(A). Recall that the space RF(A) is composed by all sums of a real number r and a fuzzy number qA, where A is a given asymmetric fuzzy number and q is an arbitrary real number. Two advantages of this space are that it can be equipped with a Banach space structure and, similar to additive models in Statistics, its elements can be interpreted as the sum of a deterministic expected/predictable value with an uncertain/noise component. The foundation of variational calculus theory requires the definition and establishment of many concepts and results. This article presents a total order relation on RF(A) for which the notions of local minimal and maximal of a RF(A)-valued function f can be derived. We present a fuzzy version of the first and second optimality conditions in terms of derivatives of f. Finally, we present a generalized fuzzy version of du Bois–Reymond lemma which is essential in variational calculus theory.
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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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