计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (12): 137-143.DOI: 10.3778/j.issn.1002-8331.2003-0224

• 模式识别与人工智能 • 上一篇    下一篇

一般多粒度量化软粗糙集模型

刘玉锋,孙文鑫   

  1. 1.重庆城市科技学院,重庆 402160
    2.重庆水利电力职业技术学院,重庆 402160
  • 出版日期:2021-06-15 发布日期:2021-06-10

Generalized Multi-granulation Quantization Soft Rough Set Model

LIU Yufeng, SUN Wenxin   

  1. 1.Chongqing Metropolitan College of Science and Technology, Chongqing 402160, China
    2.Chongqing Water Resources and Electric Engineering College, Chongqing 402160, China
  • Online:2021-06-15 Published:2021-06-10

摘要:

建立了适应数据误差、具有知识容错能力的一般多粒度量化软粗糙集模型,弥补了多粒度软粗糙集模型的不足。讨论了一般多粒度量化软粗糙近似算子的性质以及程度多粒度软粗糙集与一般多粒度量化软粗糙集之间的关系。研究了一般多粒度量化软粗糙集的不确定度量和性质。用传染病这一案例展现了一般多粒度量化软粗糙集模型的应用实效。

关键词: 多粒度, 量化, 软集, 粗糙集, 传染病

Abstract:

A general multi-granularity quantization soft rough set model is established, which adapts to data errors and has the ability of knowledge fault tolerance. The properties of a general multi-granularity quantization soft rough approximation operators and the relationship between the grade soft rough set model and a general multi-granularity quantization soft rough set model are discussed. The uncertainty measures and properties of general multi-granularity quantization soft rough sets are studied. A case of infection diseases is presented to demonstrate the application effectiveness of the general multi-granularity quantization soft rough set model.

Key words: multi-granulation, quantization, soft set, rough set, infection diseases