Recurrent Broad Learning Systems for Time Series Prediction

Authors:  Meiling Xu ; Min Han ; C. L. Philip Chen ; Tie Qiu

M. Xu, M. Han, C. L. P. Chen and T. Qiu, “Recurrent Broad Learning Systems for Time Series Prediction,” in IEEE Transactions on Cybernetics.
doi: 10.1109/TCYB.2018.2863020

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.


Min Han (M’95-A’03-SM’06) received the B.S. and M.S. degrees from the Department of Electrical Engineering, Dalian University of Technology, Dalian, China, and the M.S. and Ph.D. degrees from Kyushu University, Fukuoka, Japan, in 1982, 1993, 1996, and 1999, respectively. Since 2003, she has been a Professor with the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. She serves as a deputy director of the Chinese society of instrumentation youth work committee, a director of the institute of Liaoning province system simulation, a committee member of the Chinese society of artificial intelligence, a consultant of Jiangsu province department of science and technology, a deputy director of the institute of fuzzy information processing and machine intelligence of Dalian University of Technology, and an Organizing Chair of ISNN2013, ICICIP 2014, ICIST2016. She has visited to the Washington University in St. Louis, USA, in 2009. She has authored five books and over 300 articles in international journals and conference proceedings.

Tie Qiu Dr. Tie Qiu (M’12-SM’16) received Ph.D degree in computer science from Dalian University of Technology in 2012. He is currently Full Professor at School of Computer Science and Technology, Tianjin University, China. Prior to this position, he held assistant professor (2008) and associate professor (2013) at School of Software, Dalian University of Technology. He was a visiting professor at electrical and computer engineering at Iowa State University in U.S. (2014-2015). He serves as an area editor of Ad Hoc Networks (Elsevier), associate editor of IEEE Access Journal, Computers and Electrical Engineering (Elsevier), Human-centric Computing and Information Sciences (Springer), and International Journal on AdHoc Networking Systems, a guest editor of Future Generation Computer Systems. He serves as General Chair, Program Chair, Workshop Chair, Publicity Chair, Publication Chair or TPC Member of a number of international conferences. He has authored/co-authored 9 books, over 100 scientific papers in international journals and conference proceedings, such as IEEE/ACM ToN, IEEE TMC, TII, TIP, TCY, TITS, TVT, TNNLS, IEEE Communications Surveys and Tutorials, IEEE Communications, IEEE Systems Journal, IEEE IoT Journal, Computer Networks, IEEE SMC, ICC, CSCWD etc. There are 6 papers listed as ESI highly cited papers. He has contributed to the development of 3 copyrighted software systems and invented 10 patents. He is a senior member of China Computer Federation (CCF) and a Senior Member of IEEE and ACM.