Geometry Construction Method for Intuitionistic Fuzzy Entropy  and Its Application

doi:10.3969/j.issn.1000-3428.2013.11.049

Geometry Construction Method for Intuitionistic Fuzzy Entropy and Its Application

(1. School of Mathematical Sciences, Anhui University, Hefei 230601, China; 2. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China)

(1. School of Mathematical Sciences, Anhui University, Hefei 230601, China; 2. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China)

Received:2012-08-15 Online:2013-11-15 Published:2013-11-13

直觉模糊熵的几何构造方法及其应用

(1. 安徽大学数学科学学院，合肥 230601；2. 安徽大学计算智能与信号处理教育部重点实验室，合肥 230039)

(1. 安徽大学数学科学学院，合肥 230601；2. 安徽大学计算智能与信号处理教育部重点实验室，合肥 230039)

作者简介:毛军军(1973－)，女，副教授、博士，主研方向：智能计算；姚登宝，博士研究生；刘二宝、王翠翠，硕士研究生
基金资助:
安徽省高等学校省级自然科学研究基金资助重点项目(KJ2013A033)；安徽大学研究生创新基金资助项目(yfc100017, yfc1 00018)；安徽大学学术创新团队基金资助项目(KJTD001B)

Abstract

Abstract: Focusing on combining effects of uncertain information with the fuzziness and intuitionism and the continuous change of intuitionistic fuzzy entropy, this paper presents two geometry construction methods for intuitionistic fuzzy entropy. The classical construction criterion of intuitionistic fuzzy entropy is refined. By use of geometry methods, two new intuitionistic fuzzy entropies are presented through establishing isentropic plane and isentropic circle and distance, respectively, and relative properties are also proved. At the same time, the general construction method of intuitionistic fuzzy entropy is discussed, and compared with other existed entropy formulas by example. The reasonability and correction of the proposed methods are verified by applying the intuitionistic fuzzy entropy in multi-attributes decision-making problem.

Key words: intuitionistic fuzzy entropy, geometry, criterion, isentropic plane, isentropic circle, multi-attribute decision-making

摘要： 针对直觉模糊集中模糊性和直觉性对不确定信息的综合影响以及直觉模糊熵连续变化等问题，提出2种直觉模糊熵的几何构造方法，完善传统直觉模糊熵的构造准则。利用几何方法，分别通过建立等熵平面和等熵圆弧，结合距离构造2种新的直觉模糊熵，并证明其相关性质。论述直觉模糊熵的一般构造方法，对算例与现存的一些熵公式进行比较分析。将直觉模糊熵应用到多属性决策中，验证该方法的正确性和合理性。

关键词: 直觉模糊熵, 几何, 准则, 等熵平面, 等熵圆弧, 多属性决策

CLC Number:

TP18

MAO Jun-jun, YAO Deng-bao, LIU Er-bao, WANG Cui-cui. Geometry Construction Method for Intuitionistic Fuzzy Entropy and Its Application[J]. Computer Engineering.

毛军军，姚登宝，刘二宝，王翠翠. 直觉模糊熵的几何构造方法及其应用[J]. 计算机工程.

/ Recommend / Download Citations

URL:

https://www.ecice06.com/EN/Y2013/V39/I11/218

References

参考文献 [1] Atanassov K. Intuitionistic Fuzzy Sets[J]. Fuzzy Sets and Systems, 1986, 20(1): 87-96. [2] Zadeh L A. Fuzzy Sets[J]. Information and Control, 1965, 8(3): 338-353. [3] Atanassov K. More on Intuitionistic Fuzzy Sets[J]. Fuzzy Sets and Systems, 1989, 33(1): 37-46. [4] Atanassov K. New Operations Defined over the Intuitionistic Fuzzy Sets[J]. Fuzzy Sets and Systems, 1994, 61(2): 137-142. [5] Burillo P, Bustince H. Entropy on Intuitionistic Fuzzy Sets and on Interval-valued Fuzzy Sets[J]. Fuzzy Sets and Systems, 1996, 78(3): 305-316. [6] Szmidt E, Kacprzyk J. Entropy for Intuitionistic Fuzzy Sets[J]. Fuzzy Sets and Systems, 2001, 118(3): 467-477. [7] 王毅, 雷英杰. 一种直觉模糊熵的构造方法[J]. 控制与决策, 2007, 22(12): 1390-1394. [8] 王培, 魏翠萍. 一种区间直觉模糊熵的构造方法[J]. 计算机工程与应用, 2011, 47(2): 43-45. [9] 范平. Vague集模糊熵的约束条件及其度量方法分析[J]. 计算机应用研究, 2010, 27(12): 4502-4504. [10] Zhang Hongying, Zhang Wenxiu, Mei Changlin. Entropy of Interval-valued Fuzzy Sets Based on Distance and Its Relationship with Similarity Measure[J]. Knowledge-based Systems, 2009, 22(6): 449-454. [11] Li Jinquan, Deng Guannan, Li Hongxing, et al. The Relationship Between Similarity Measure and Entropy of Intuitionistic Fuzzy Sets[J]. International Journal of Information Sciences, 2012, 188: 314-321. [12] 万树平. 基于商圈的多传感器目标识别方法[J]. 系统工程与电子技术, 2009, 31(3): 500-502. [13] Vlachos I K, Sergiadis G D. Intuitionistic Fuzzy Information——Applications to Pattern Recognition[J]. Pattern Recognition Letters, 2007, 28(2): 197-206. [14] 徐泽水. 不确定多属性决策方法及应用[M]. 北京: 清华大学出版社, 2005. 编辑顾逸斐

[1]	ZHOU Qinyuan, DENG Yueping, ZHANG Lei, ZHANG Chen, LU Rirong, HU Xianzhe. Dynamic Visual SLAM System Integrating Optical Flow and Multi-View Geometry [J]. Computer Engineering, 2024, 50(5): 250-259.
[2]	Xiaoying DU, Qingni YUAN, Jianyou QI, Chen WANG, Feilong DU, Ao REN. Visual SLAM Method Based on Semantic Segmentation in Dynamic Scenes [J]. Computer Engineering, 2024, 50(3): 242-249.
[3]	Xiaolong WANG, Bo JIANG. Safety Helmet Wearing Detection Based on Improved YOLOX-m [J]. Computer Engineering, 2023, 49(12): 252-261.
[4]	Zhanwei HOU, Xin YANG, Zihao SHEN, Hui WANG, Peiqian LIU. Multi-attribute Decision-making Method for Secure Anonymous Set Construction [J]. Computer Engineering, 2023, 49(11): 169-177, 186.
[5]	CAI Ruichu, WU Siyu, QIAO Jie. Study of Hawkes Process for Granger Causality Discovery Among Faults [J]. Computer Engineering, 2023, 49(1): 65-72.
[6]	FANG Lijin, WANG Keqi. A Visual SLAM Algorithm for Motion Redetection Combined with Deep Learning [J]. Computer Engineering, 2022, 48(5): 18-26.
[7]	TANG Jiamin, HAN Hua, HUANG Li. Coarse-grained and Fine-grained Features Extraction Based on Unsupervised Learning in Pedestrian Re-identification [J]. Computer Engineering, 2022, 48(4): 269-275,283.
[8]	WANG Shuyan, HU Qianhua, SUN Jiaze. Software Test Data Augmentation Method Based on Beetle Antennae Search [J]. Computer Engineering, 2021, 47(9): 191-196.
[9]	SHAO Lijie, MA Fumin. Two-Phase Information Granulation Combined with Interval Type-2 FRCM and Mixed Metrics [J]. Computer Engineering, 2021, 47(6): 88-97.
[10]	YE Peiwen, JIA Xiangdong, YANG Xiaorong, NIU Chunyu. Collaborative Edge and Cloud Offloading for Internet of Vehicles Using Multi-Agent Reinforcement Learning [J]. Computer Engineering, 2021, 47(4): 13-20.
[11]	ZHANG Bingyu, PAN Qing, TIAN Nili, Everett Xiaolin Wang. A Source Number Estimation Method Based on Multiple Feature Fusion [J]. Computer Engineering, 2021, 47(4): 115-119,126.
[12]	CHEN Xu, JIANG Ye. Research on Recording Playback Attack Detection Based on Mixed Features of Gaussian Filter Bank [J]. Computer Engineering, 2021, 47(3): 291-297,303.
[13]	JIN Yazhou, ZHANG Zhengjun, YAN Zihan, WANG Yaping. Optimized Ranking Algorithm Based on Margin Criterion for Multi-Label Learning [J]. Computer Engineering, 2020, 46(7): 104-109.
[14]	TANG Suqin, LIU Xiaomei, YUAN Lei. Graph Representation Learning Method Based on Neural Ranking with Embedded Hyperbolic Layer [J]. Computer Engineering, 2020, 46(6): 81-87.
[15]	ZHU Mingqiang, FU Xiaodong, LIU Li, FENG Yong, LIU Lijun. Online Service Evaluation Method Based on Slater Social Choice Theory [J]. Computer Engineering, 2020, 46(2): 126-133.

Please choose a citation manager

Content to export