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The belief rule base model performs well in continuous systems but not in piecewise systems, and it suffers from a combinatorial explosion when there are a large number of model input referential parameters, resulting in a slow model optimization speed. Aiming at these problems, we do the following things. Firstly, different from the existing methods that improve model performance by improving optimization algorithm, we propose a distributed belief rule base model construction and inference methodology, which reduces the algorithm complexity of model construction and inference and improves the model accuracy by dividing the model into several small and independent subsets of belief rule base. Then we prove that the distributed belief rule base model can reduce the optimization algorithm complexity by using this methodology optimizes all subsets in parallel and independently during model construction, and reduce the inference cost of inactivated rules by adopting a hierarchical inference methodology. Then we prove that the proposed distributed belief rule base model performs well on both continuous and piecewise systems; also, construction efficiency and inference accuracy using the same optimization algorithm are higher than those of the traditional belief rule base model through experiments of nonlinear continuous function fitting and binary piecewise function fitting. Finally, we improve that the proposed distributed belief rule base model has a noticeable performance advantage in complex environments compared to the traditional model through the network situation prediction experiment and point out that the distributed belief rule base model is suitable in applications that require high real-time performance and high accuracy.

A Novel Construction and Inference Methodology of Belief Rule Base