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

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 net...

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Bibliographic Details
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
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Summary: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.
ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12663