[1]邓廷权,,杨成东,等.模糊相似关系下变精度模糊粗糙集[J].智能系统学报,2012,7(02):148-152.
 DENG Tingquan,YANG Chengdong,et al.Fuzzy similarity relation based variable precision fuzzy rough sets[J].CAAI Transactions on Intelligent Systems,2012,7(02):148-152.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年02期
页码:
148-152
栏目:
出版日期:
2012-04-25

文章信息/Info

Title:
Fuzzy similarity relation based variable precision fuzzy rough sets
文章编号:
1673-4785(2012)02-0148-05
作者:
邓廷权1 2杨成东2 3张月童1
1. 哈尔滨工程大学 理学院, 黑龙江 哈尔滨 150001;
2. 哈尔滨工程大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001;
3. 临沂大学 信息学院, 山东 临沂 276000
Author(s):
DENG Tingquan1 2 YANG Chengdong2 3 ZHANG Yuetong1
1. College of Science, Harbin Engineering University, Harbin 150001, China;
 2. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
3. School of Informatics, Linyi University, Linyi 276000, China
关键词:
变精度粗糙集 模糊相似关系 相对错误包含度 模糊逻辑算子 模糊粗糙集
Keywords:
variable precision rough sets fuzzy similarity relation relative error rates of classification fuzzy logical operator fuzzy rough sets
分类号:
TPl8; TP301
文献标志码:
A
摘要:
经典变精度模糊粗糙集模型是基于模糊等价关系建立的.在实际应用中, 模糊等价关系很难直接构造, 需要通过求模糊相似关系的传递闭包生成.对模糊关系的这种改造会丢失较多有价值的信息, 而且还增大了模糊粗糙集应用的计算复杂度.基于模糊逻辑算子构造2个模糊集的相对错误包含度, 构造性地提出基于模糊相似关系的变精度模糊粗糙集模型, 研究了该模型的性质.该模型一方面具有变精度粗糙集的优点,对噪声数据具有很好的容错能力, 另一方面是基于模糊相似关系建立的, 其应用范围更为广泛.
Abstract:
A classical variable precision fuzzy rough set was built on the basis of fuzzy equivalent relationships. Nonetheless, it is hard to directly obtain the fuzzy equivalent relationships, which are usually replaced by a closure of fuzzy equivalent relationships, and this method causes a loss of much valuable information and increases the computation complexity in the application of the fuzzy rough set. This paper first took advantage of a fuzzy logical operator to construct fuzzy relative error rates of classification, and then proposed a variable precision fuzzy rough set model based on fuzzy similarity relationships. Moreover, the properties of this model were investigated. On the one hand, the model is able to deal with noise data with the advantages of variable precision rough sets; on the other hand, since it is based on fuzzy similarity relationships, the model could be applied more widely. 

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2010-09-19.
网络出版日期:2012-04-16.
基金项目:国家自然科学基金资助项目(10771043); 水下机器人国防技术重点实验室基金资助项目(002010260730).
通信作者:杨成东.            E-mail:yangchengdong2008@163.com.
作者简介:
邓廷权,男,1965年生,教授,博士生导师,主要研究方向为模糊信息分析、数学形态学与图像分析、智能识别与计算机视觉等.主持国家自然科学基金、中国博士后科学基金、黑龙江省博士后科学基金等多项科研项目.近年来,发表学术论文30余篇,其中10余篇论文被SCI、EI、ISPT等检索.
杨成东,男,1984年生,博士研究生,主要研究方向为数据挖掘、智能计算、粗糙集理论及其应用等.
 张月童,男,1982年生,硕士研究生,主要研究方向为智能计算、粗糙集理论等.
更新日期/Last Update: 2012-07-12