Weighted Broad Learning System and Its Application in Nonlinear Industrial Process Modeling

Broad learning system (BLS) is a novel neural network with effective and efficient learning ability. BLS has attracted increasing attention from many scholars owing to its excellent performance. This article proposes a weighted BLS (WBLS) based on BLS to tackle the noise and outliers in an industria...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 31; no. 8; pp. 3017 - 3031
Main Authors: Chu, Fei, Liang, Tao, Chen, C. L. Philip, Wang, Xuesong, Ma, Xiaoping
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Automation
Broad learning system (BLS)
Control engineering
Heuristic algorithms
incremental learning algorithm
Learning
Learning algorithms
Learning systems
Machine learning
Modelling
Neural networks
Noise
noise and outliers
Outliers (statistics)
Process control
Retraining
Training
weighted penalty factor
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Broad learning system (BLS) is a novel neural network with effective and efficient learning ability. BLS has attracted increasing attention from many scholars owing to its excellent performance. This article proposes a weighted BLS (WBLS) based on BLS to tackle the noise and outliers in an industrial process. WBLS provides a unified framework for easily using different methods of calculating the weighted penalty factor. Using the weighted penalty factor to constrain the contribution of each sample to modeling, the normal and abnormal samples were allocated higher and lower weights to increase and decrease their contributions, respectively. Hence, the WBLS can eliminate the bad effect of noise and outliers on the modeling. The weighted ridge regression algorithm is used to compute the algorithm solution. Weighted incremental learning algorithms are also developed using the weighted penalty factor to tackle the noise and outliers in the additional samples and quickly increase nodes or samples without retraining. The proposed weighted incremental learning algorithms provide a unified framework for using different methods of computing weights. We test the feasibility of the proposed algorithms on some public data sets and a real-world application. Experiment results show that our method has better generalization and robustness.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2019.2935033