您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（理学版）》

《山东大学学报(理学版)》 ›› 2022, Vol. 57 ›› Issue (3): 31-40.doi: 10.6040/j.issn.1671-9352.4.2021.114

• • 上一篇    

粒空间中划分知识的正交补研究

王宝丽1,2,王涛2,廉侃超1*,韩素青2,3   

  1. 1. 运城学院数学与信息技术学院, 山西 运城 044000;2. 太原师范学院数学系, 山西 晋中 030619;3. 太原师范学院计算机系, 山西 晋中 030619
  • 发布日期:2022-03-15
  • 作者简介:王宝丽(1982— ), 女, 博士,副教授,研究方向为数据挖掘与智能决策的研究工作. E-mail:pollycomputer@163.com*通信作者简介:廉侃超(1974— ),女,副教授,研究方向为优化计算及数字媒体表达的研究工作. E-mail:lkc333@126.com
  • 基金资助:
    国家自然科学基金资助项目(61703363,61806116);山西省应用基础研究计划项目(201901D211462,201901D211461);山西省高等学校青年科研人员培育计划项目;山西省重点实验室开放课题基金项目(CICIP2018008);运城学院博士启动资金资助项目(YQ-2016006)

Research of partition knowledge orthogonal complement for granular space

WANG Bao-li1,2, WANG tao2, LIAN Kan-chao1*, HAN Su-qing2,3   

  1. 1. School of Mathematics &
    Information Technology, Yuncheng University, Yuncheng 044000, Shanxi, China;
    2. Department of Mathematics, Taiyuan Normal University, Jinzhong 030619, Shanxi, China;
    3. Department of Computer Science, Taiyuan Normal University, Jinzhong 030619, Shanxi, China
  • Published:2022-03-15

PDF (PC)

159

摘要： 从知识代数格结构的视角出发, 分析人类精确识别目标概念与知识目标的学习特征, 提出了划分知识的绝对正交补与相对正交补的概念, 进一步结合划分知识的差异性度量, 提出了能够刻画绝对认知过程中的极大差异性、相对认知中的易迁移性的极大绝对正交补、极小相对正交补。

关键词: 划分知识, 绝对正交补, 相对正交补, 极大绝对正交补, 极小相对正交补

Abstract: From the algebraic perspective, this study proposes the concepts of the absolute orthogonal complements and the relative orthogonal complements for the starting partition knowledge by analyzing the study characteristic of accurately discerning the objective concept or knowledge. Moreover, we put forward the thoughts of maximal absolute orthogonal complement and minimal relative orthogonal complement to model the most significant difference and the easiest transferring in the absolute and relative cognition of agents.

Key words: partition knowledge, absolute orthogonal complement, relative orthogonal complement, maximal absolute orthogonal complement, minimal relative orthogonal complement

中图分类号: 

  • TP391
[1] YAO Yiyu. A triarchic theory of granular computing[J]. Granular Computing, 2016, 1(2):145-157.
[2] 梁吉业,钱宇华,李德玉,等.大数据挖掘的粒计算理论与方法[J]. 中国科学: 信息科学, 2015, 45(11):1355-1369. LIANG Jiye, QIAN Yuhua, LI Deyu, et al. Theory and method of granular computing for big data mining[J]. Science in China: Series F: Information Sciences, 2015, 45(11):1355-1369.
[3] 王国胤,张清华,马希骜,等. 知识不确定性问题的粒计算模型[J]. 软件学报, 2011, 22(4):676-694. WANG Guoyin, ZHANG Qinghua, MA Xiao, et al. Granular computing models for knowledge uncertainty[J]. Journal of Software, 2011, 22(4):676-694.
[4] YAO Jingtao, VASILAKOS Athanasios V, PEDRYCZ Witold. Granular computing: perspectives and challenges[J]. IEEE Transactions on Cybernetics, 2013, 43:1977-1989.
[5] 苗夺谦,徐菲菲,姚一豫,等. 粒计算的集合论描述[J]. 计算机学报, 2012, 35(2):351-363. MIAO Duoqian, XU Feifei, YAO Yiyu, et al. Set-theoretic formulation of granular computing[J]. Chinese Journal of Computers, 2012, 35(2):351-363.
[6] 徐怡,姚一豫. 划分序乘积空间:基于划分的粒计算模型[J]. 计算机研究与发展, 2019, 56(4):836-843. XU Yi, YAO Yiyu. Partition order product space: partition based granular computing model[J]. Journal of Computer Research and Development, 2019, 56(4):836-843.
[7] YAO Yiyu. A partition model of granular computing[M] //Transactions on Rough Sets I. Berlin: Springer, 2004, 232-253.
[8] YAO Yiyu, ZHANG Nan, MIAO Duoqian, et al. Set-theoretic approaches to granular computing[J]. Fundamenta Informaticae, 2012,115(2/3):247-264.
[9] WU Weizhi, LEUNG Yee. Theory and applications of granular labelled partitions in multi-scale decision tables[J]. Information Sciences, 2011, 181(18):3878-3897.
[10] 苗夺谦,范世栋. 知识的粒度计算及其应用[J]. 系统工程理论及实践, 2002, 22(1):48-56. MIAO Duoqian, FAN Shidong. The calculation of knowledge granulation and its application[J]. Systems Engineering-Theory & Practice, 2002, 22(1):48-56.
[11] LIANG Jiye, QIAN Yuhua. Information granules and entropy in information systems[J]. Science in China: Series F: Information Sciences, 2008, 51(10), 1427-1444.
[12] QIAN Yuhua, LIANG Jiye, WU Weizhi, et al. Information granularity in fuzzy binary GrC model[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(2):253-264.
[13] 黄国顺,曾凡智,陈广义,等. 基于严凸函数的知识粒度与相对粒度[J]. 模式识别与人工智能, 2013, 26(10):897-909. HUANG Guoshun, ZENG Fanzhi, CHEN Guangyi, et al. Knowledge granularity and relative granularity based on strictly convex function[J]. Pattern Recognition and Artificial Intelligence, 2013, 26(10):897-909.
[14] YAO Yiyu, ZHAO Liquan. A measurement theory view on the granularity of partitions[J]. Information Sciences, 2012, 213:1-13.
[15] 王国胤,张清华. 不同知识粒度下粗糙集的不确定性研究[J]. 计算机学报, 2008, 31(9):1588-1598. WANG Guoying, ZHANG Qinghua. Uncertainty of rough sets in different knowledge graularities[J]. Chinese Journal of Computers, 2008, 31(9):1588-1598.
[16] YAO M X. Granularity measures and complexity measures of partition-based granular structures[J]. Knowledge-Based Systems, 2019, 163(1):885-897.
[17] 王宝丽,梁吉业. 信息系统中的知识距离与知识粗糙熵[J]. 计算机科学, 2007, 34(3):151-154 WANG Baoli, LIANG Jiye. Knowledge distance and rough entropy in information systems[J]. Computer Science, 2007, 34(3):151-154.
[18] QIAN Yuhua, LI Yebin, LIANG Jiye, et al. Fuzzy granular structure distance[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(6):2245-2259.
[19] QIAN Yuhua, CHENG Honghong, WANG Jieting, et al. Grouping granular structures in human granulation intelligence[J]. Information Sciences, 2017, 382:150-169.
[20] YANG Jie, WANG Guoyin, ZHANG Qinghua, et al. Knowledge distance measure in multigranulation spaces of fuzzy equivalence relations[J]. Information Sciences, 2018, 448:18-35.
[21] YANG Jie, WANG Guoyin, ZHANG Qinghua, et al. Knowledge distance measure for the multigranularity approximations of a fuzzy concept[J]. IEEE Transactions on Fuzzy Systems, 2020, 28(4):706-717.
[22] 王宝丽,姚一豫. 信息表中的约简补集对及其一般定义[J]. 南京大学学报(自然科学), 2020, 56(4):461-468. WANG Baoli, YAO Yiyu. A definition of comple-mentary attribute reduction pairs in an information table[J]. Journal of Nanjing University(Natural Science), 2020, 56(4):461-468.
[23] 李金海,闫梦宇,徐伟华,等. 概念认知学习的若干问题与思考[J]. 西北大学学报(自然科学版), 2020, 4:501-515. LI Jinhai, YAN Mengyu, XU Weihua, et al. Some problems and thoughts on concept-cognitive learning[J]. Journal of Northwest University(Natural Science Edition), 2020, 50(4):501-515.
[24] WANG Changzhong, HUANG Yang, DING Weiping, et al. Attribute reduction with fuzzy rough self-information measures[J]. Information Sciences, 2021, 549:68-86.
[25] ZHANG Xianyong, YAO Hong, LV Zhiying, et al. Class-specific information measures and attribute reducts for hierarchy and systematicness[J]. Information Sciences, 2021, 563:196-225.
[1] 易三莉,陈建亭,贺建峰. ASR-UNet: 一种基于注意力机制改进的视网膜血管[J]. 《山东大学学报(理学版)》, 2021, 56(9): 13-20.
[2] 王静红,梁丽娜,李昊康,周易. 基于注意力网络特征的社区发现算法[J]. 《山东大学学报(理学版)》, 2021, 56(9): 1-12,20.
[3] 王伟玉, 史存会, 俞晓明, 刘悦, 程学旗. 一种事件粒度的抽取式话题简短表示生成方法[J]. 《山东大学学报(理学版)》, 2021, 56(5): 66-75.
[4] 张一鸣,王国胤,胡军,傅顺. 基于密度峰值和网络嵌入的重叠社区发现[J]. 《山东大学学报(理学版)》, 2021, 56(1): 91-102.
[5] 许侃,刘瑞鑫,林鸿飞,刘海峰,冯娇娇,李家平,林原,徐博. 基于异质网络嵌入的学术论文推荐方法[J]. 《山东大学学报(理学版)》, 2020, 55(11): 35-45.
[6] 张凌,任雪芳. 数据智能分类与分类智能检索-识别[J]. 《山东大学学报(理学版)》, 2020, 55(10): 7-14.
[7] 林明星. 基于变分结构引导滤波的低照度图像增强算法[J]. 《山东大学学报(理学版)》, 2020, 55(9): 72-80.
[8] 王佳麒,杨沐昀,赵铁军,赵臻宇. 检务文书检索数据集的构建[J]. 《山东大学学报(理学版)》, 2020, 55(7): 81-87.
[9] 余鹰,吴新念,王乐为,张应龙. 基于标记相关性的多标记三支分类算法[J]. 《山东大学学报(理学版)》, 2020, 55(3): 81-88.
[10] 温柳英,袁伟. 多标签符号型属性值划分的聚类方法[J]. 《山东大学学报(理学版)》, 2020, 55(3): 58-69.
[11] 张敏情,周能,刘蒙蒙,王涵,柯彦. 基于Paillier的同态加密域可逆信息隐藏[J]. 《山东大学学报(理学版)》, 2020, 55(3): 1-8,18.
[12] 王新乐,杨文峰,廖华明,王永庆,刘悦,俞晓明,程学旗. 基于多维度特征的主题标签流行度预测[J]. 《山东大学学报(理学版)》, 2020, 55(1): 94-101.
[13] 李妮,关焕梅,杨飘,董文永. 基于BERT-IDCNN-CRF的中文命名实体识别方法[J]. 《山东大学学报(理学版)》, 2020, 55(1): 102-109.
[14] 杨亚茹, 王永庆, 张志斌, 刘悦, 程学旗. 基于多元信息融合的用户关联模型[J]. 《山东大学学报(理学版)》, 2019, 54(9): 105-113.
[15] 张迪,查东东,刘华勇. 带两类形状参数的三次λμ-α-DP曲线的构造[J]. 《山东大学学报(理学版)》, 2019, 54(9): 114-126.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(理学版)》编辑部　
地址: 山东省济南市山大南路27号山东大学中心校区（250100）　电话: +86-0531-88366917　E-mail: xblxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发