Paper Info
Title
Recurrent Broad Learning Systems for Time Series Prediction.
Abstract
The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of “fine-tuning” in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.
Year
DOI
Venue
2020
10.1109/TCYB.2018.2863020
IEEE transactions on cybernetics
Keywords
Field
DocType
Artificial neural networks,Time series analysis,Learning systems,Zinc,Predictive models,Complex systems,Feedforward systems
Conjugate gradient method,Complex system,Time series,Sequential data,Autoencoder,Incremental learning,Artificial intelligence,Deep learning,Chaotic,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
50
4
2168-2267
Citations 
PageRank 
References 
18
0.64
0
Authors
4
Name
Order
Citations
PageRank
Meiling Xu1674.11
Min Han21648.79
C. L. Philip Chen34022244.76
Tie Qiu489580.18