[1]陈楠楠,巩晓婷,傅仰耿.基于改进规则激活率的扩展置信规则库推理方法[J].智能系统学报,2019,14(6):1179-1188.[doi:10.11992/tis.201906046]
CHEN Nannan,GONG Xiaoting,FU Yanggeng.Extended belief rule-based reasoning method based on an improved rule activation rate[J].CAAI Transactions on Intelligent Systems,2019,14(6):1179-1188.[doi:10.11992/tis.201906046]
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CHEN Nannan,GONG Xiaoting,FU Yanggeng.Extended belief rule-based reasoning method based on an improved rule activation rate[J].CAAI Transactions on Intelligent Systems,2019,14(6):1179-1188.[doi:10.11992/tis.201906046]
基于改进规则激活率的扩展置信规则库推理方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第6期
页码:
1179-1188
栏目:
学术论文—知识工程
出版日期:
2019-11-05
- Title:
- Extended belief rule-based reasoning method based on an improved rule activation rate
- Keywords:
- belief rule base; data driven; evidence reasoning; individual matching degree; k-nearest neighbors; none activation; consistency; completeness
- 分类号:
- TP18
- 摘要:
- 数据驱动的扩展置信规则库系统，是在传统置信规则库的基础上利用关系数据来生成规则，使用该方法构建规则库简单有效。然而，该方法激活的规则存在不一致与不完整，并且该方法无法处理零激活的输入。鉴于此，本文提出基于改进规则激活率的扩展置信规则库方法，通过高斯核改进个体匹配度计算方法，权衡激活规则的一致性与完整性，并利用k近邻思想解决规则零激活问题。最后，本文选取非线性函数拟合实验和输油管道检漏实验来检验所提方法的效率和准确度。实验结果表明该方法既保证了扩展置信规则库系统的推理效率，也提高了推理结果的精度。
- Abstract:
- The data-driven extended belief rule-based system uses relational data to generate rules based on the traditional belief rule base. Using this method to build a rule base is simple and effective. However, the rules activated by this method are inconsistent and incomplete, and this method cannot handle none-activated inputs. Therefore, this paper proposes an extended belief rule-based method, based on an improved rule activation rate. This method improves upon the individual matching degree calculation method through gauss kernels, balances the consistency and completeness of activation rules, and solves the problem of non-activation of rules based on the idea of k-nearest neighbors. Finally, this paper selects a nonlinear function fitting experiment and an oil pipeline leak detection experiment to test the efficiency and accuracy of the proposed method. Experimental results showed that the proposed method not only ensures efficiency, but also improves the accuracy of the extended belief rule-based system.
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备注/Memo
收稿日期:2019-06-24。
基金项目:国家自然科学基金项目（61773123）；福建省自然科学基金项目（2019J01647）.
作者简介:陈楠楠,男,1993年生,硕士研究生,主要研究方向为智能决策与专家系统;巩晓婷,女,1982年生,讲师,主要研究方向为不确定多准则决策、信息隐藏技术。参与国家自然科学基金项目3项、福建省自然科学基金项目2项和教育部高等学校博士学科点专项科研基金项目1项。发表学术论文10余篇;傅仰耿,男,1981年生,副教授,博士,CCF会员,CAAI会员,主要研究方向为决策理论与方法、数据挖掘、机器学习、智能系统。主持国家自然科学基金项目1项、福建省自然科学基金项目2项。获国家发明专利授权2项,发表学术论文3 0余篇。
通讯作者:傅仰耿.E-mail:ygfu@qq.com
更新日期/Last Update:
2019-12-25