A behavior prediction method for complex system based on belief rule base with structural adaptive

Predicting the behavior of complex systems and taking appropriate measures for system management is of paramount importance for decision-makers. Belief rule base (BRB) is an effective method for modeling complex systems, and its construction relies on expert knowledge. However, in certain complex system prediction problems, deriving the structure and parameters of BRB from limited expert knowledge or existing models is a challenge, and there may even be a lack of available expert knowledge. Data mining plays a crucial role in obtaining the parameters for model construction in the design of decision support systems (DSSs). Therefore, this paper proposes a method for behavior prediction of complex systems called the structural adaptive BRB (SA-BRB). First, to reduce the randomness of K-means+ +, this paper introduces an error constraint and employs this algorithm to mine historical data for constructing a reference value set. Second, a model ensemble construction process is designed to create different model structures. Subsequently, the evidence reasoning (ER) algorithm is applied to derive the models, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is used for model optimization. Finally, a model evaluation method is established, allowing adaptive adjustment of the model structure according to the needs of engineering practice and the preferences of decision-makers. Furthermore, the effectiveness of the proposed method is validated through two case studies: one focusing on predicting the health status of a flywheel system and the other on forecasting full-load power generation in a combined power plant.