在区间值直观模糊环境下选择一个最佳方法来减少能源危机
Dilshad Alghazzawi1, Hanan Alolaiyan2, Humaira Ashfaq3
1Department of Mathematics, College of Science & Arts, King Abdul Aziz University, Rabigh, Saudi Arabia.
Scientific reports
|April 15, 2024
概括
本研究引入了一个新的相似度衡量区间值的直觉模糊集合,解决现有方法的局限性. 这项新奇的措施增强了决策和模式识别,在可再生能源选择中有应用.
科学领域:
- 模糊的集合理论 模糊的集合理论
- 决策科学 决策科学 决策科学
背景情况:
- 间隔值的直观模糊集合 (IIFS) 对于表示复杂的现实数据至关重要.
- 类似性测量对于IIFS环境中的决策和模式识别至关重要.
- 对IIFS的现有相似性测量有限制,这阻碍了它们的实际应用.
研究的目的:
- 为了解决当前对区间值直观模糊集合的相似性测量的缺点.
- 为区间值直观模糊集合提出一种新的相似度测量方法.
- 通过示例和比较来证明新措施的有效性和有效性.
主要方法:
- 为区间值直观模糊集合开发一种新的相似度量.
- 确定拟议措施的基本属性.
- 设计一种算法,用于使用新措施进行多标准决策.
- 该措施应用于可再生能源选择问题.
主要成果:
- 成功开发了一种用于区间值直观模糊集合的新型相似度测量方法.
- 拟议的措施克服了现有方法的局限性.
- 该措施的有效性通过比较分析和实际案例研究来验证.
- 设计了一种使用新措施进行多标准决策的算法.
结论:
- 新提出的相似度测量在区间值直观模糊环境中是有效和有效的.
- 该措施为决策和模式识别任务提供了显著的改进.
- 对可再生能源选择的应用证明了其在应对现实世界挑战方面的实际实用性.
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