Researches on rough truth of rough axioms based on granular computing

被引:1
作者
Yan, Lin [1 ]
Yan, Shuo [2 ]
机构
[1] College of Computer and Information Engineering, Henan Normal University
[2] School of Computer and Information Technology, Beijing Jiaotong University
基金
中国国家自然科学基金;
关键词
Granular computing; Operator; Rough axiom; Rough truth; Rough validity;
D O I
10.4304/jsw.9.2.265-273
中图分类号
学科分类号
摘要
The concept, rough truth, is first presented in rough logic. It is a logical value, and lies between truth and falsity. By combining rough logic with modal logic, rough validity of the rough axioms is studied in this paper, which has close links with the logical value: rough truth. Because an axiom of modal logic corresponds to a rough axiom, the study of this paper actually focuses on the analysis of rough truth of the axioms in modal logic, which is based on a structure constructed in this paper. The structure is linked with a series of special states. The research on rough truth connects the special states with the rough axioms. At the same time, granular computing is introduced to the research process. As an approach to data processing, granular computing plays an important role in determining whether a rough axiom is roughly true or not. Thus, the study also demonstrates a way of research on granular computing. The conclusions show that each rough axiom is roughly true at every state of the structure, which means that each rough axiom is roughly valid. This is the desired result. © 2014 Academy Publisher.
引用
收藏
页码:265 / 273
页数:8
相关论文
共 50 条
  • [31] AN AGENT DECISION SUPPORT MODULE BASED ON GRANULAR ROUGH MODEL
    El-Ghamrawy, Sally M.
    Eldesouky, Ali I.
    INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2012, 11 (04) : 793 - 820
  • [32] Granular representation of OWA-based fuzzy rough sets
    Palangetic, Marko
    Cornelis, Chris
    Greco, Salvatore
    Slowinski, Roman
    FUZZY SETS AND SYSTEMS, 2022, 440 : 112 - 130
  • [33] A novel granular ball computing-based fuzzy rough set for feature selection in label distribution learning
    Qian, Wenbin
    Xu, Fankang
    Huang, Jintao
    Qian, Jin
    KNOWLEDGE-BASED SYSTEMS, 2023, 278
  • [34] A rough-granular computing in discovery of process models from data and domain knowledge
    NGUYEN Hung Son
    SKOWRON Andrzej
    重庆邮电大学学报(自然科学版), 2008, (03) : 341 - 347
  • [35] A survey on granular computing and its uncertainty measure from the perspective of rough set theory
    Yunlong Cheng
    Fan Zhao
    Qinghua Zhang
    Guoyin Wang
    Granular Computing, 2021, 6 : 3 - 17
  • [36] A rough-granular computing in discovery of process models from data and domain knowledge
    Nguyen, Hung Son
    Skowron, Andrzej
    2008 INTERNATIONAL FORUM ON KNOWLEDGE TECHNOLOGY, 2008, : 341 - 347
  • [37] A model of granular computing with applications. Granules from rough inclusions in information systems
    Polkowski, L.
    2006 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, 2006, : 9 - 16
  • [38] A survey on granular computing and its uncertainty measure from the perspective of rough set theory
    Cheng, Yunlong
    Zhao, Fan
    Zhang, Qinghua
    Wang, Guoyin
    GRANULAR COMPUTING, 2021, 6 (01) : 3 - 17
  • [39] On the granular representation of fuzzy quantifier-based fuzzy rough sets
    Theerens, Adnan
    Cornelis, Chris
    INFORMATION SCIENCES, 2024, 665
  • [40] Ordered granular labeled structures and rough approximations
    Wu, Weizhi
    Gao, Cangjian
    Li, Tongjun
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2014, 51 (12): : 2623 - 2632