裸体模拟的一些理论基础
Michel Broniatowski1, Wolfgang Stummer2
1Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université, 4 Place Jussieu, 75252 Paris, France.
Entropy (Basel, Switzerland)
|April 26, 2024
概括
本研究介绍了一般化的φ-分歧,用于测量各种模糊集合类型和基本信念赋值之间的不相似性. 一种新的裸体模拟方法有效地解决了相关的受约束优化问题.
科学领域:
- 信息理论是信息理论.
- 统计 统计 统计 统计
- 机器学习是机器学习.
- 人工智能的人工智能是人工智能.
背景情况:
- 在信息理论和相关领域,量化信息不相似性至关重要.
- 限制优化对于处理不准确或模糊的数据至关重要.
研究的目的:
- 为模糊集,n-rung orthopair模糊集和基本信念赋值定义概括的φ-分歧.
- 开发方法来解决涉及这些差异的受约束最小化问题.
主要方法:
- 对于模糊的集合类型和基本信念赋值的一般化φ-分歧的定义.
- 应用无维度裸体 (纯) 模拟方法进行优化.
- 对于差异计算的基本信念赋值的重新调整.
主要成果:
- 在多个模糊集合表示中成功定义了泛化的φ-分歧.
- 展示裸体模拟方法在解决受约束最小化问题的有效性.
- 将这种方法扩展到基本信念的分配.
结论:
- 概括的φ-分歧为信息不相似性提供了可靠的衡量标准.
- 裸体模拟方法为信息理论中的优化问题提供了一种有效的方法.
- 该框架适用于各种数据表示,包括模糊集和信念赋值.
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