Premium
Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration
Author(s) -
Kuok SinChi,
Yuen KaVeng
Publication year - 2021
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12663
Subject(s) - probabilistic logic , computer science , architecture , artificial intelligence , network architecture , bayesian network , probabilistic neural network , machine learning , artificial neural network , feed forward , bayesian probability , scheme (mathematics) , deep learning , engineering , control engineering , time delay neural network , mathematics , art , mathematical analysis , computer security , visual arts
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.