A New Multilayer Belief Rule Base Model for Complex System Modeling
You Cao, Zhijie Zhou, Guanyu Hu, Changhua Hu, Shuaiwen Tang, Gailing Li
Abstract
Belief rule base (BRB) model suffers from the rule explosion problem when it is applied in modeling complex systems. The rule explosion problem makes it difficult to establish and optimize the BRB model effectively. Aiming at this problem, a new multilayer BRB (MLBRB) model is proposed, which consists of the extracting block and the processing block. In the extracting block, the inputs are first divided into two groups, and then each group is used to construct a hierarchical BRB model. The outputs of extracting block are treated as the inputs of the processing block. To obtain the optimal MLBRB, the layerwise learning strategy and the layer adaptive growth strategy are proposed to optimize these two blocks, respectively. A case study for the safety assessment of the liquefied natural gas storage tank is conducted to verify the effectiveness of the proposed method.