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基于区间型 Pythagorean 模糊 Schweizer 和 Sklar 幂集结算子的稻米质量管理决策的新方法。

A novel approach to decision making in rice quality management using interval-valued Pythagorean fuzzy Schweizer and Sklar power aggregation operators.

机构信息

Software Engineering Institute of Guangzhou, Guangzhou, China.

School of Mathematics and Information Science, Guangzhou University, Guangzhou, China.

出版信息

PLoS One. 2024 Oct 24;19(10):e0311525. doi: 10.1371/journal.pone.0311525. eCollection 2024.

Abstract

The Pythagorean fuzzy set and interval-valued intuitionistic fuzzy set are the basis of the interval-valued Pythagorean fuzzy set (IVPFS) which offers an effective approach to addressing the complex uncertainty in decision-analysis processes, making it applicable across a broad spectrum of applications. This paper introduces several aggregation operators within the IVPF framework, such as the interval-valued Pythagorean fuzzy SS power weighted average operator, and the interval-valued Pythagorean fuzzy SS power geometric operator using the notion of power aggregation operators through Schweizer and Sklar (SS) operations. The existence of SS t-norms and t-conorms in the IVPF framework for addressing multi-attribute decision-making problems gives the generated operator's ability to make the information aggregation approach more adaptable compared to other current ones. The application of the proposed approach holds the potential to enhance crop yield, optimize resource utilization, and contribute to the overall sustainability of agriculture. Additionally, sensitivity and comparative analyses are provided to clarify the stability and dependability of the results acquired through this approach.

摘要

在区间值 Pythagorean 模糊集(IVPFS)中,Pythagorean 模糊集和区间值直觉模糊集是基础,它为解决决策分析过程中的复杂不确定性提供了一种有效的方法,适用于广泛的应用领域。本文在 IVPF 框架内引入了几种聚合算子,如区间值 Pythagorean 模糊 SS 幂权平均算子和区间值 Pythagorean 模糊 SS 幂几何算子,使用 Schweizer 和 Sklar(SS)操作的幂聚合算子的概念。在 IVPF 框架中存在 SS t-范数和 t-模,可以解决多属性决策问题,使得生成算子的信息聚合方法比其他现有方法更具适应性。该方法的应用有可能提高作物产量,优化资源利用,为农业的整体可持续性做出贡献。此外,还提供了敏感性和比较分析,以阐明通过这种方法获得的结果的稳定性和可靠性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ecc/11500917/34b03257c480/pone.0311525.g001.jpg

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