Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration

• Published in: Computer-aided civil and infrastructure engineering 2021-10, Vol.36 (10), p.1270-1287
• Main Authors: Kuok, Sin‐Chi, Yuen, Ka‐Veng
• Format: Article
• Language: English
• Subjects: Augmentation; Bayesian analysis; Computer architecture; Configurations; Machine learning; Neural networks; Nonparametric statistics; Optimization; Probabilistic models; Statistical analysis; Structural health monitoring
• ISSN: 1093-9687; 1467-8667
• DOI: 10.1111/mice.12663

Broad Bayesian learning (BBL), a novel probabilistic Bayesian neural network methodology with optimized architecture configuration, is proposed. It has an expandable feedforward broad learning network. Therefore, the uncertain estimates can be quantified in terms of probability distributions and network architecture augmentation can be adopted incrementally by use of the inherited information from the previously trained network. Furthermore, a learning network architecture configuration optimization scheme is proposed to determine the optimal architecture configuration. Based on the plausibilities of the concerned configurations, the most plausible one can be obtained, and it indicates the proper augmentation to develop the optimal configuration. To demonstrate the proposed methodology, three simulation examples and an application with in‐field structural health monitoring measurement are presented.