A Caltech Library Service

Robust sparse Bayesian learning for broad learning with application to high-speed railway track monitoring

Wang, Chenyue and Gao, Jingze and Li, Hui and Lin, Chao and Beck, James L. and Huang, Yong (2022) Robust sparse Bayesian learning for broad learning with application to high-speed railway track monitoring. Structural Health Monitoring . ISSN 1475-9217. doi:10.1177/14759217221104224. (In Press)

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


In this study, we focus on non-parametric probabilistic modeling for general regression analysis with large amounts of data and present an algorithm called the robust sparse Bayesian broad learning system. Robust sparse Bayesian learning is employed to infer the posterior distribution of the sparse connecting weight parameters in broad learning system. Regardless of the number of candidate features, our algorithm can always produce a compact subset of hidden-layer neurons of almost the same size learned from the data, which allows the algorithm to automatically adjust the model complexity of the network. This algorithm not only solves the regression problem of large amounts of data robustly but also possesses high computational efficiency and low requirements for computing hardware. Moreover, as a Bayesian probabilistic algorithm, it can provide the posterior uncertainty quantification of the predicted output, giving a measure of prediction confidence. The proposed algorithm is verified using simulated data generated by a benchmark function and also applied in non-parametric probabilistic modeling using high-speed railway track monitoring data. The results show that compared with several existing neural network algorithms, our proposed algorithm has strong model robustness, excellent prediction accuracy, and computational efficiency for regression analysis with large amounts of data, and has the potential to be widely used in general regression problems in science and engineering.

Item Type:Article
Related URLs:
URLURL TypeDescription
Wang, Chenyue0000-0001-5241-4545
Li, Hui0000-0001-9198-3951
Huang, Yong0000-0002-7963-0720
Funding AgencyGrant Number
National Natural Science Foundation of China52078174
National Key Research and Development Program of China2021YFF0501003
China Association for Science and Technology2021QNRC001
Record Number:CaltechAUTHORS:20220913-660236900
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116903
Deposited By: Tony Diaz
Deposited On:04 Oct 2022 22:23
Last Modified:04 Oct 2022 22:23

Repository Staff Only: item control page