基于模糊系统的定性与定量知识的综合集成

陈德旺; 刘俐俐; 赵文迪; 欧纪祥; 孙艳焱; 郑楠

doi:10.11959/j.issn.2096-6652.202447

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基于模糊系统的定性与定量知识的综合集成

学术论文 | 更新时间：2025-01-24

- 基于模糊系统的定性与定量知识的综合集成
- Qualitative and quantitative knowledge of metasynthesis based on fuzzy system
- 智能科学与技术学报 2024年6卷第4期页码：445-455
- 作者机构：
  
  1.福建理工大学交通运输学院，福建福州 350118
  2.福建省北斗导航与智慧交通协同创新中心，福建福州 350118
  3.中国科学院自动化研究所多模态人工智能系统全国重点实验室，北京 100190
- 作者简介：
  
  [ "陈德旺（1976- ），男，博士，福建理工大学交通运输学院闽江学者特聘教授，俄罗斯自然科学院外籍院士，欧洲自然科学院院士。主要研究方向为人工智能、模糊系统、智能交通系统。曾在20多个国际会议的项目委员会任职。自2015年以来，一直担任IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS的副编辑。发表论文200余篇，出版专著7部，获得科研奖励和人才奖励20余项。" ]
  [ "刘俐俐（1999- ），女，福建理工大学交通运输学院硕士生，主要研究方向为模糊系统、自动驾驶。" ]
  [ "赵文迪（1996- ），男，福建理工大学交通运输学院硕士生，主要研究方向为智能交通、模糊系统。" ]
  [ "欧纪祥（2001- ），男，福建理工大学交通运输学院硕士生，主要研究方向为模糊系统、可解释人工智能。" ]
  [ "孙艳焱（1999- ），女，福建理工大学交通运输学院硕士生，主要研究方向为模糊系统、线路规划。" ]
  [ "郑楠（1984- ），中国科学院自动化研究所多模态人工智能系统全国重点实验室副研究员，主要研究方向为复杂系统、综合集成、数据挖掘。" ]
- 基金信息：
  
  国家重点研究发展计划(2023YFC3304104);福建省第三批创新之星人才计划(GY-Z23068);福建理工大学科研启动基金项目;Scientific Research Foundation of Fujian University of Technology(GY-Z22071)
- DOI：10.11959/j.issn.2096-6652.202447
  中图分类号： TP273
- 收稿日期：2024-03-08，
  
  修回日期：2024-06-20，
  
  纸质出版日期：2024-12-15
- 稿件说明：
移动端阅览
陈德旺,刘俐俐,赵文迪等.基于模糊系统的定性与定量知识的综合集成[J].智能科学与技术学报,2024,06(04):445-455.

CHEN Dewang,LIU Lili,ZHAO Wendi,et al.Qualitative and quantitative knowledge of metasynthesis based on fuzzy system[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):445-455.
陈德旺,刘俐俐,赵文迪等.基于模糊系统的定性与定量知识的综合集成[J].智能科学与技术学报,2024,06(04):445-455. DOI： 10.11959/j.issn.2096-6652.202447.

CHEN Dewang,LIU Lili,ZHAO Wendi,et al.Qualitative and quantitative knowledge of metasynthesis based on fuzzy system[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):445-455. DOI： 10.11959/j.issn.2096-6652.202447.

摘要

综合集成法已被广泛应用于处理复杂系统相关问题，其核心是人机结合、从定性到定量的迭代求解，然而如何描述定性与定量知识，如何有效融合定性与定量知识仍是亟须解决的问题。模糊系统模拟了人脑推理过程，既可以利用专家的定性知识，也能够从数据中学习模糊规则，使用规则映射的方式实现对不确定性问题的系统决策。将模糊系统引入定性与定量知识的描述理解与融合过程，提出了一种基于模糊系统的可解释性综合集成法。该方法从定性和定量两个角度分别获取知识，再将两种知识综合集成，形成模糊规则库，完成模糊系统建模。该方法可以有效地将专家经验和数据学习相结合，增强模型的可解释性，提高复杂系统决策过程的鲁棒性和科学性，有望成为未来综合集成法研究的一种实现途径，更好地解决现实世界复杂系统的相关问题。

Abstract

The metasynthesis has been widely used to solve complex system problems

and its core is the combination of human and machine

from qualitative to quantitative iterative solving. But how to describe and integrate qualitative and quantitative knowledge effectively is still the urgent problem to be solved. The fuzzy system simulates the reasoning process of human brain. It can not only use the qualitative knowledge of experts

but also learn fuzzy rules from the data

and use the way of rule mapping to realize the system decision of uncertain problems. By introducing the fuzzy system into the process of description

comprehension and fusion of qualitative and quantitative knowledge

the interpretable metasynthesis based on fuzzy system was proposed. Knowledge was obtained from quantitative and qualitative perspectives

and then the two kinds of knowledge were integrated to form a fuzzy rule base and completed the fuzzy system modeling. This method effectively combines expert experience with data learning

enhances the interpretability of the model

and improves the robustness and scientificity of the decision-making process of complex systems. This method is expected to be a realization method for the research of integrated method in the future

so as to better solve the problem of system complexity in the real world.

关键词

Keywords

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