Complex system modeling using deviation-smoothing belief rule base with training and optimization

In the operation of complex systems, damage caused by aging and other factors can significantly impact their safety. Consequently, rational modeling approaches are essential for ensuring the stable and reliable operation of such systems. Due to their intricate internal structures and the influence of external environments, models of complex systems confront numerous uncertainties. The belief rule base (BRB), as an effective artificial intelligence (AI) tool for knowledge representation and reasoning, is capable of handling uncertain information through data-driven and knowledge-driven approaches, making it suitable for complex system modeling. However, existing BRB modeling approaches consider only a single interval when calculating rule matching degrees, which can lead to the issue of zero activation of rules; additionally, some rules may lack relevance and rationality in actual systems, thereby increasing model complexity and affecting performance due to redundancy. Therefore, a complex system modeling system based on deviation-smoothing belief rule base (DS-BRB) has been proposed. Firstly, this approach employs a matching degree calculation approach that integrates bias computation with smoothing techniques, enabling effective rule activation over a broader range of intervals. Subsequently, a specialized rule reduction approach designed for rule-level analysis is utilized, iteratively reducing rules based on their weights. Finally, the proposed methodology is evaluated using pipeline leakage detection and various public benchmark datasets. Finally, experiments on pipeline leakage detection and benchmark datasets demonstrate that DS-BRB resolves zero activation, achieves higher modeling accuracy with a reduced rule base, and highlights BRB's potential as an interpretable AI tool for complex system modeling.

Authors

• Chao Sun
• Jiahao Mai
• Wei He
• Hailong Zhu
• Qi Liu

Keywords

• Artificial intelligence
• Belief rule base
• Rule reduction
• Rule activation
• Complex system modeling
• Expert system