Please use this identifier to cite or link to this item:
Title: Explainable data-driven optimization for complex systems with non-preferential multiple outputs using belief rule base
Authors: Chang, Leilei
Zhang, Limao
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Chang, L. & Zhang, L. (2021). Explainable data-driven optimization for complex systems with non-preferential multiple outputs using belief rule base. Applied Soft Computing, 110, 107581-.
Project: 04INS000423C120
Journal: Applied Soft Computing
Abstract: 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 optimization model is constructed where the optimization objective is the integration of multi-outputs and respective constraints are designed. Then, a new optimization algorithm with a new customized gene makeup is designed where the NPMO–BRB inferencing process is embedded in the fitness calculation procedure. A practical case study on Changsha Metro Line 4 is studied to use multiple geological parameters to infer multiple operational parameters. Case study results show that NPMO–BRB has shown superior performance in comparison with the random forest (RF), the backpropagation neural network (BPNN), the Gradient Gaussian Process (GPR), as well as multiple separate BRBs. Owing to the explainability provided by the NPMO–BRB approach, further investigations into the belief distribution comparison reveal more information that can be used as practical work guidelines.
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2021.107581
Schools: School of Civil and Environmental Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

Citations 50

Updated on Sep 24, 2023

Web of ScienceTM
Citations 50

Updated on Sep 15, 2023

Page view(s)

Updated on Sep 23, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.