Explainable data-driven optimization for complex systems with non-preferential multiple outputs using belief rule base

• Main Authors: Chang, Leilei, Zhang, Limao
• 其他作者: School of Civil and Environmental Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Computer science and engineering; Belief Rule Base; Optimization Algorithms
• 在線閱讀: https://hdl.handle.net/10356/160256

To better handle problems with non-preferential multi-outputs (NPMO), a new approach is proposed in this study by employing the belief rule base (BRB) to provide a superior nonlinearity modeling ability as well as good explainability. The new approach is thus called NPMO–BRB. First, a new optimizati...