基于概念近似的认知学习

吴雨青; 林艺东; 梁涛巨

doi:10.16152/j.cnki.xdxbzr.2026-03-012

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基于概念近似的认知学习

粒计算与认知学习 | 更新时间：2026-07-07

- 基于概念近似的认知学习
- Cognitive learning based on concept approximation
- 西北大学学报（自然科学版） 2026年56卷第3期页码：596-607
- 作者机构：
  
  1.闽南师范大学　数学与统计学院，福建　漳州　363000
  2.数字福建气象大数据研究所，福建　漳州　363000
  3.福建省粒计算及其应用重点实验室，福建　漳州　363000
- 作者简介：
  
  吴雨青，女，硕士研究生，从事粒计算与概念认知学习研究，Wuyq_517@163.com。
  林艺东，男，博士，副教授，从事不确定性理论及其应用研究，yidong_lin@yeah.net。
- 基金信息：
  
  国家自然科学基金(12201284);福建省自然科学基金(2022J05169)
- DOI：10.16152/j.cnki.xdxbzr.2026-03-012
  中图分类号： TP182
- 收稿：2025-11-10，
  
  修回：2025-11-30，
  
  纸质出版：2026-06-25
- 稿件说明：
移动端阅览
吴雨青, 林艺东, 梁涛巨. 基于概念近似的认知学习[J]. 西北大学学报（自然科学版）, 2026,56(3):596-607.

WU Yuqing, LIN Yidong, LIANG Taoju. Cognitive learning based on concept approximation[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 596-607.
吴雨青, 林艺东, 梁涛巨. 基于概念近似的认知学习[J]. 西北大学学报（自然科学版）, 2026,56(3):596-607. DOI： 10.16152/j.cnki.xdxbzr.2026-03-012.

WU Yuqing, LIN Yidong, LIANG Taoju. Cognitive learning based on concept approximation[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 596-607. DOI： 10.16152/j.cnki.xdxbzr.2026-03-012.

摘要

概念认知学习通过模拟人脑认知过程学习新知识，是一个新兴的交叉领域。然而，现有的概念认知学习模型忽略了概念外延与内涵的契合性、概念空间的冗余性、概念空间的结构特征等问题。因此，本文基于概念近似提出一种新的概念认知学习模型。首先，通过构造属性对象λ算子生成不完全概念，从而提高概念外延与内涵的契合性。针对概念空间的冗余性，进一步探讨概念的上近似概念集、下近似概念集以及边界概念集。随后，考虑到概念空间的结构特征，构建上近似概念空间和下近似概念空间，并提出新的学习精度以刻画认知学习效果。最后，通过仿真实验验证该模型的有效性。

Abstract

Concept-cognitive learning is an emerging cross-cutting field that learns new knowledge by modeling the cognitive processes of the human brain. However

the existing concept-cognitive learning models have ignored the problems of the fit between the extent and intent of concepts

the redundancy and structural characteristics of concept space as well. To address these challenges

this paper introduces a novel cognitive learning model grounded in the concept of approximation. Firstly

incomplete concepts are generated by constructing attribute-object λ operator and thus the fit between the extent and intent of concepts is improved. Aiming at the redundancy of concept space

further discussion is conducted on the set of upper approximation concepts

the collection of lower approximation concepts

and the family of boundary concepts. Subsequently

by taking into account the structural characteristics of concept space

the upper and lower approximation concept spaces are established respectively

and a novel learning accuracy is then proposed to portray the cognitive learning effect. Finally

the effectiveness of the proposed method is verified by simulation experiments.

关键词

Keywords

references

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