[1]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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Extended belief rule-based reasoning method based on an improved rule activation rate

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1179-1188 Column: 学术论文—知识工程 Public date: 2019-11-05
Title:
Extended belief rule-based reasoning method based on an improved rule activation rate
Author(s):
CHEN Nannan1;  GONG Xiaoting2;  FU Yanggeng1; 2
1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;
2. Decision Sciences Institute, Fuzhou University, Fuzhou 350116, China
Keywords:
belief rule base; data driven; evidence reasoning; individual matching degree; k-nearest neighbors; none activation; consistency; completeness
CLC:
TP18
DOI:
10.11992/tis.201906046
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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