算法的变化来实现信念函数的最大
Joaquín Abellán1, Alejandro Pérez-Lara1, Serafín Moral-García1
1Department of Computer Science and Artificial Intelligence, University of Granada, 18014 Granada, Spain.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
证据理论 (TE) 使用最大 (ME) 来量化信息,但它的计算是复杂的. 本研究介绍了一种修改后的算法,它减少了计算步骤,提高了TE中ME在不完整信息场景中的适用性.
科学领域:
- 信息理论 信息理论
- 决策科学 决策科学 决策科学
- 数学基础数学基础的基础
背景情况:
- 经典的概率理论 (PT) 与不准确或不完整的信息作斗争.
- 证据理论 (TE) 为不精确的概率提供了一个框架,其中最大的 (ME) 量化了证据.
- ME的计算复杂性限制了其在TE中的实际应用.
研究的目的:
- 解决与证据理论 (TE) 中计算最大 (ME) 相关的计算挑战.
- 为ME计算提出一个修改后的算法,以提高效率.
- 增强ME在TE的实用适用性,用于涉及不确定的数据的现实问题.
主要方法:
- 在证据理论 (TE) 中开发了计算最大 (ME) 的现有算法的变化.
- 修改后的算法专注于在每个计算步骤中减少可能的功率集的大小.
- 在所需的计算步骤数量方面分析了新算法的效率.
主要成果:
- 修改后的算法大大减少了计算最大 (ME) 所需的步骤数量.
- 通过代地减少功率集大小,可以降低计算成本.
- 拟议的变化为 TE 内的 ME 计算提供了更有效的方法.
结论:
- 修改后的算法提供了一种更易于计算的方法,用于确定证据理论 (TE) 中的最大 (ME).
- 这一改进预计将增加ME在TE信息量化中的采用和实用性.
- 这项研究有助于在具有不准确或不完整数据的领域更广泛地应用 TE.
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