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一种基于对形状和距离的双重考虑的三角云模型的新型相似度测量
Jianjun Yang1,2,3, Jiahao Han1, Qilin Wan1
1School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China.
PeerJ. Computer science
|September 14, 2023
概括
一个新的EPTCM算法通过准确测量不确定的概念相似性来增强AI. 这种认知计算方法改进了现有的方法,用于像图像检索和数据聚类等应用.
科学领域:
- 人工智能的人工智能
- 认知计算是一种认知计算.
- 数据科学数据科学数据科学
背景情况:
- 测量不确定的概念之间的相似性对于AI应用至关重要.
- 现有的云模型相似性算法存在差异歧视和不稳定的结果.
- 云模型为认知计算和相似度测量提供了一个有希望的方法.
研究的目的:
- 为了解决当前云模型相似性测量算法的局限性.
- 提出一种新的算法,用于提高不确定的概念的相似性测量.
- 为了提高AI应用中的识别和分类准确性.
主要方法:
- 开发了EPTCM算法,利用三角模糊数EW型接近度和云落差差.
- 纳入考虑云模型中的形状和距离相似性.
- 评估了EPTCM算法与LICM,OECM,FDCM和MSCM等既定方法进行对比.
主要成果:
- 与现有方法相比,EPTCM算法显示出更高的识别和分类准确性.
- 实验结果证实了EPTCM算法的有效性,克服了之前的算法缺陷.
- 拟议的方法在相似性测量中显示出更好的稳定性和区分能力.
结论:
- EPTCM算法是衡量不确定的概念相似性的有效和可行的解决方案.
- 这种新的方法显著推进了人工智能应用的认知计算.
- 对于各种AI任务,EPTCM方法提供了更准确,更可靠的替代方案.
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