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一种新的决策方法,用于选择最好的深度学习技术在对数分数模糊集合信息下
Naseer Ullah1, Muhammad Azam2, Muhammad Abbas3
1School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, 100124, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了对数分数模糊集 (Log-FFS) 和新的聚合运算符,以改进人工智能中的数据表示. 这些方法提高了选择深度学习技术的决策能力.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 模糊的集合理论 模糊的集合理论
背景情况:
- 深度学习 (DL) 是现代AI和ML进步的组成部分.
- 有效的数据表示对于DL流程至关重要.
- 模糊集 (FS) 提供了超越清晰集的通用数据表示.
研究的目的:
- 介绍一个新的模糊集类:对数分数模糊集 (Log-FFS).
- 为Log-FFS.开发基于对数原理的新聚合运营商.
- 将这些新的方法应用于决策问题,特别是选择DL技术.
主要方法:
- 逻辑分数模糊集 (Log-FFS) 的定义.
- 聚合运算符的制定,如对数分数模糊加权平均值 (Log-FFWA).
- 将COPRAS和扩展TOPSIS方法与Log-FFS.集成在一起
主要成果:
- 定义和分析了新型日志FFS和相关的聚合运算符.
- 证明了基于Log-FFS的决策方法的适用性.
- 在选择最佳深度学习技术的成功应用.
结论:
- 记录-FFS为人工智能中的数据表示提供了一个有效的框架.
- 建议的聚合运营商和决策方法是稳健的.
- 这种方法为深度学习技术的选择提供了最佳的结果.
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