分布轮廓与局部特征融合的云模型不确定性相似度量

代劲; 胡彪; 王国胤; 张磊

doi:10.11999/JEIT210033

分布轮廓与局部特征融合的云模型不确定性相似度量

doi: 10.11999/JEIT210033

代劲^{1, 2},
胡彪¹,
王国胤^{1, 3, ,},
张磊¹

1.
重庆邮电大学计算智能重庆市重点实验室重庆 400065
2.
重庆邮电大学软件工程学院重庆 400065
3.
重庆邮电大学旅游多源数据感知与决策技术文化和旅游部重点实验室重庆 400065

基金项目: 国家自然科学基金 (61936001, 61772096)，重庆市自然科学基金(cstc2019jcyj-cxttX0002)

详细信息

作者简介:
代劲：男，1978年生，教授，研究方向为大数据知识工程、智能信息处理

胡彪：男，1995年生，硕士生，研究方向为智能信息处理、数据挖掘

王国胤：男，1970年生，教授，研究方向为粒计算、认知计算、智能信息处理

张磊：男，1995年生，硕士生，研究方向为智能信息处理、数据挖掘

通讯作者:
王国胤　wanggy@cqupt.edu.cn

中图分类号: TP18
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- 被引次数: 0
出版历程
- 收稿日期: 2021-01-11
- 修回日期: 2021-07-15
- 网络出版日期: 2021-08-26
- 刊出日期: 2022-04-18

The Uncertainty Similarity Measure of Cloud Model Based on the Fusion of Distribution Contour and Local Feature

DAI Jin^{1, 2},
HU Biao¹,
WANG Guoyin^{1, 3
, ,},
ZHANG Lei¹

1.
Chongqing Key laboratory of Computation Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3.
Key Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism, Chongqing University of Posts and Telecommunication, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61936001, 61772096), The Natural Science Foundation of Chongqing (cstc2019jcyj-cxttX0002)

摘要

摘要: 针对当前基于云模型的不确定性相似度量或为精确局部数据的量化计算，或单纯通过其整体几何特征进行度量，导致结果具有较大的片面性问题，综合考虑云模型整体几何特征与微观云滴分布贡献，该文提出了一种分布轮廓与局部特征融合的不确定性相似度量方法，即基于包络带及其云滴贡献度的云模型不确定性相似度量方法(EACCM)。该方法将体现云模型几何特征的包络带(内包络曲线和外包络曲线之间区域)作为相似度量基础，结合其重叠部分包含云滴的贡献度大小建立综合度量模型。仿真结果显示，该方法度量结果更为科学合理，并且有效避免了同一数字特征相差较大或者非常接近时导致的相似性异常问题。
- 云模型 /
- 相似性度量 /
- 正态分布 /
- 包络带 /
- 贡献度
Abstract: In view of the fact that the current uncertainty similarity measurement based on cloud model is either the quantitative calculation of accurate local data, or the measurement only through its overall geometric features, which leads to the result with great one sidedness. In this paper, considering the overall geometric features of cloud model and the contribution of micro cloud droplets distribution, an uncertainty similarity measurement method based on the fusion of distribution contour and local features is proposed, i.e. the Envelope Area of the Contribution based on Cloud Model (EACCM). In this method, the envelope (the area between the inner envelope curve and the outer envelope curve) which reflects the geometric characteristics of the cloud model is used as the basis of similarity measurement, and a comprehensive measurement model is established based on the contribution of cloud droplets contained in the overlapped part. The simulation results show that the method is more scientific and reasonable, and can effectively avoid the problem of abnormally similarity caused by the large difference or very close of the same digital features.
- Cloud model /
- Similarity measurement /
- Normal distribution /
- Envelope /
- Contribution

HTML全文

图 1 正态云模型(0,3,0.3)

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图 2 正态云模型(0,3,0.3)的包络带

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图 3 云${C_1}(0,2,0.2)$和${C_2}(4,2,0.2)$包络带重叠区域

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图 4 云${C_1}(0,2,0.2)$和${C_2}(1,2,0.1)$包络带重叠区域

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图 7 云模型${C_1}$和${C_2}$相对位置随${\rm{E}}{{\rm{n}}_2}$和${\rm{H}}{{\rm{e}}_2}$的变化

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图 5 云模型${C_1}$和${C_2}$相对位置随${\rm{E}}{{\rm{x}}_2}$, ${\rm{E}}{{\rm{x}}'_2}$的变化

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图 6 相似度随${\rm{E}}{{\rm{x}}_2}$, ${\rm{E}}{{\rm{x}}'_2}$, ${\rm{E}}{{\rm{n}}_2}$和${\rm{H}}{{\rm{e}}_2}$变化趋势

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图 8 目标云T与各标尺云相交情况

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图 9 不同度量方法分类的正确率

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表 1 能力等级划分对应的子区间及评估标尺

等级描述	区间	云模型
优	[68.8, 100.0]	C₅(100.0, 10.3, 1.04)
良	[49.9, 88.3]	C₄(69.1, 6.37, 0.64)
中	[38.0, 62.0]	C₃(50.0, 3.93, 0.39)
差	[11.7, 50.1]	C₂(30.9, 6.37, 0.64)
极差	[0, 31.2]	C₁(0, 10.3, 1.04)

下载: 导出CSV

表 2 目标云与各标尺云的相似度

方法	极差(C₁)	差(C₂)	中(C₃)	良(C₄)	优(C₅)
CCM	0.00	0.01	0.01	0.13	0.46
MMDCM	0.00	0.00	0.00	0.14	0.44
文献[17]	0.01	0.53	0.74	0.90	0.92
文献[24]	0.00	0.00	0.00	0.10	0.38
本文	0.00	0.00	0.00	0.11	0.15

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