Linear fuzzy partial differential equations for A-linearly correlated fuzzy processes

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2024-11-08 DOI:10.1016/j.ins.2024.121629

M. Shahidi, L.C. Barros, E. Esmi

引用次数: 0

Abstract

In this paper, we deal with linear fuzzy partial differential equations (FPDEs) whose solutions correspond to A-linearly correlated fuzzy processes. More precisely, we investigate general second-order linear fuzzy partial differential equations and provide solutions for two essential cases: the fuzzy advection equation and the fuzzy wave-like equation with fuzzy velocity terms. One of the advantages of our approach is that these FPDEs can be converted into a classical system of partial differential equations (PDEs). Thus, any suitable and appropriate method to solve PDEs can be applied to solve this classical system and, eventually leading to the derivation of a fuzzy solution. Finally, we provide some examples to demonstrate our results.

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A 线性相关模糊过程的线性模糊偏微分方程

本文讨论线性模糊偏微分方程 (FPDE)，其解与 A 线性相关模糊过程相对应。更确切地说，我们研究了一般的二阶线性模糊偏微分方程，并提供了两种基本情况的解法：模糊平流方程和带有模糊速度项的模糊类波方程。我们的方法的优点之一是，这些 FPDE 可以转换成经典的偏微分方程（PDE）系统。因此，任何合适的 PDE 求解方法都可以用于求解这个经典系统，并最终推导出模糊解。最后，我们提供了一些例子来展示我们的成果。

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

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.