多尺度集值决策信息系统
作者:
作者单位:

1.华侨大学数学科学学院;2.华侨大学数学学院

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通讯作者:

中图分类号:

TP301

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)(NO11871259);福建省高校创新团队发展计划项目,泉州市高层次人才团队项目(2017ZT012)资助.


Multi-Scale Set Value Decision Information System
Author:
Affiliation:

School of Mathematical Sciences, Huaqiao University

Fund Project:

National Natural Science Foundation of China(No.11871259); Program for Innovative Research Team in Science and Technology in Fujian Province University, and Quanzhou High-Level Talents Support Plan ( No. 2017ZT012).

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    摘要:

    集值信息系统中的对象的属性值多值化,以达到对复杂信息更全面的刻画. 在传统的集值信息系统中,每 个属性只有一个尺度. 但在具体的应用过程中,人们往往需要在不同的尺度上处理和分析数据. 为此,本文首次将 多尺度信息系统的粒度转换函数引入集值信息系统中,建立了多尺度集值信息系统的理论框架. 并讨论该系统的不 同尺度间信息粒、粗糙集的关系.在此基础上,建立了多尺度集值决策信息系统的粒计算模型. 并讨论了该模型不 同尺度间协调性的传递性质.然后,我们讨论了协调和不协调的多尺度集值决策信息系统的最优尺度选择方法.本文 改进了多尺度决策信息系统的粒计算模型,在理论分析和实际应用中有一定的价值.

    Abstract:

    The set-valued information system allows the object to have attribute multi-valued to achieve a more comprehensive description of complex information. In traditional set-valued information systems, each attribute has only one scale. But in the specific application process, people often need to process and analyze the data at different scales. In this paper, the granularity transformation function of multi-scale information system is introduced into the set-valued information system for the first time, and the theoretical framework of the multi-scale set-valued information system is established. The relationship of information grains and rough sets between different scales of the system is discussed. On this basis, a multi-scale set-valued decision information system model is established. The transfer properties of consistent between different scales of the model are discussed. then, we discuss the optimal scale selection method for a consistent and inconsistent multi-scale set-valued decision information system. This paper improves the granular computing model of multi-scale decision information system, which has certain value in theoretical analysis and practical application.

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历史
  • 收稿日期:2020-07-03
  • 最后修改日期:2021-01-06
  • 录用日期:2021-01-08
  • 在线发布日期: 2021-02-04
  • 出版日期: