计算机科学
专家系统
推论
基于规则的系统
钥匙(锁)
推理规则
人工智能
复杂系统
数据挖掘
机器学习
知识库
推理机
机制(生物学)
限制
计算机安全
认识论
工程类
哲学
机械工程
作者
Chunchao Zhang,Zhijie Zhou,You Cao,Shuaiwen Tang,Pengyun Ning,Leiyu Chen
标识
DOI:10.1016/j.eswa.2022.119065
摘要
A belief rule base (BRB) expert system provides a generic inference framework for approximating the complicated nonlinear relationships between inputs and outputs. Such systems have been widely applied in the system health management community. However, limited by the method of construction rules, existing methods encounter the combinatorial explosion problem in the modeling of complex systems. Thus, determining how to effectively use the collected signals, system mechanism, and expert knowledge to realize the health evaluation of complex systems has become a key issue limiting the development of BRB systems. To solve this problem, a modified micro belief rule structure is proposed, and a new belief rule network (BRN) model is developed. Then, the cautious conjunctive rule is introduced to realize the fusion and reasoning of the nonindependent nodes in the BRN. In addition, the structure and parameter learning strategies of the proposed BRN are given, thereby providing a systemic mechanism to enhance the capability of the BRN when both expert knowledge and observed data are available. The effectiveness of the BRN model is verified in an aerospace relay experiment.
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