Dynamic Performance Evaluation of High-Valued Complex Systems Based on Belief Rule Base

This study presents a novel dynamic method for evaluating the performance of high-valued complex systems (HVCSs). It aims to address the following three problems in performance evaluation (PE) of this type of system, including limited observation data of system states, uncertainty in expert knowledge, and high real-time requirement. To address these challenges, we expand the discernment frame of the belief rule base (BRB) to the power set (BRB-P), enhancing its capacity to handle limited data and uncertain expert knowledge. The belief rules are integrated using the evidential reasoning (ER) algorithm, ensuring the interpretability of the framework. Simultaneously, we introduce a new optimization model to enhance the real-time performance of the framework and mitigate the explosion of belief rule combinations when dealing with multiple input features. Furthermore, we develop a utility-based framework reduction method to adaptively adjust the discernment framework of BRB-P. This enables the structure and parameters of the framework to be dynamically trained using real-time observation data. An experimental illustration is presented to demonstrate the effectiveness of the proposed framework.