关键词: 置信规则库（BRB）, 集成学习, 梯度下降法（GDA）, Bagging, AdaBoost

Abstract: Current research on belief rule base (BRB) focuses on single BRB system, however, the reasoning performance of single BRB system is influenced by the values of parameters. And the uneven distribution or small amount of training data can lead to the incompleteness of training parameters, which makes the locality of information for decision provided by reasoning results. To solve these problems, this paper proposes BRB-ensemble system base in gradient descent algorithm (GDA) via combining the Bagging and AdaBoost with BRB respectively, and the BRB system is applied to the pipeline leak detection and multimodal function fitting. The performance of BRB system can be improved by the integration of multiple sub-BRB. In the case study, the convergence accuracy and fitting effect are used to analyze the performance of BRB-ensemble, and the proposed approach is compared with other single BRB system. The experimental results show that the BRB-ensemble method is reasonable and effective.

Key words: belief rule base (BRB), ensemble learning, gradient descent algorithm (GDA), Bagging, AdaBoost