A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems

Chengdong Li; Jianqiang Yi; Yisheng Lv; Peiyong Duan

doi:10.1109/JAS.2019.1911543

Volume 6 Issue 6

Nov. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(6): 1487-1498

Chengdong Li, Jianqiang Yi, Yisheng Lv and Peiyong Duan, "A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1487-1498, Nov. 2019. doi: 10.1109/JAS.2019.1911543

Citation:

Chengdong Li, Jianqiang Yi, Yisheng Lv and Peiyong Duan, "A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1487-1498, Nov. 2019. doi: 10.1109/JAS.2019.1911543

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A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems

doi: 10.1109/JAS.2019.1911543

1.
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
2.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.
Qingdao Academy of Intelligent Industries, Qingdao 266109, China
4.
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China

Funds:

the National Natural Science Foundation of China 61473176

the National Natural Science Foundation of China 61773246

Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities ZR2015JL021

the Taishan Scholar Project of Shandong Province TSQN201812092

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Author Bio:

Chengdong Li received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2010. He is currently an Associate Professor with the School of Information and Electrical Engineering, Shandong Jianzhu University.
His major research interests include data-driven modeling and control, fuzzy logic theory and applications, fuzzy neural networks and other computational intelligence methods. His research interests also include the intelligent control methods with applications to intelligent buildings and smart homes. He has authored and co-authored over 80 papers in international journals and conferences. He has been the Program Committee Member of several international conferences and the Reviewer for several international conferences and journals. (e-mail: lichengdong@sdjzu.edu.cn)

Jianqiang Yi received the B.E. degree from the Beijing Institute of Technology, Beijing, China, in 1985, and the M.E. and Ph.D. degrees from the Kyushu Institute of Technology, Kitakyushu, Japan, in 1989 and 1992, respectively. From 1992 to 1994, he was with the Computer Software Development Company, Tokyo, Japan. From 1994 to 2001, he was a Chief Researcher at MYCOM Inc., Kyoto, Japan. Since 2001, he has been a Full Professor in the Institute of Automation, Chinese Academy of Sciences. His research interests include theories and applications of fuzzy control, adaptive control, and intelligent robotics, etc. He was an Associate Editor for IEEE Computational Intelligence Magazine in 2008−2009, and is an Associate Editor for Journal of Advanced Computational Intelligence and Intelligent Informatics, and Journal of Innovative Computing, Information and Control, etc. He has published more than 60 journal papers and nearly 200 conference papers, and holds more than 20 issued patents.(e-mail: jianqiang.yi@ia.ac.cn)

Yisheng Lv received the B.E. and M.E. degrees in transportation engineering from Harbin Institute of Technology, in 2005 and 2007, respectively, and the Ph.D. degree in control theory and control engineering from Chinese Academy of Sciences, Beijing, China, in 2010. He is currently an Associate Professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interests include traffic data analysis, dynamic traffic modeling, and parallel traffic management and control systems. (e-mail: yisheng.lv@ia.ac.cn)

Peiyong Duan received the B.Sc. degree in 1996 from Shandong University of Technology (merged into Shandong University in 2000), Shandong, China, and the Ph.D. degree in 1999 from Shanghai Jiao Tong University, Shanghai, China. From 1999 to 2014, he was with the School of Information and Electrical Engineering, Shandong Jianzhu University where appointed as an Associate Professor and a Full Professor in 1999 and 2002, respectively. From 2014 to 2015, he was with the School of Computer Science, Liaocheng University, Shandong, China. Since 2016, he has been with the School of Information Science and Engineering, Shandong Normal University, Shandong, China. His current research interests include discrete optimization and scheduling. He has authored more than 60 refereed papers.(e-mail: duanpeiyong@sdnu.edu.cn)
Corresponding author:
Yisheng Lv, e-mail: yisheng.lv@ia.ac.cn
Received Date: 2017-11-20
Revised Date: 2018-01-21
Accepted Date: 2018-03-07

Available Online: 2019-10-20

Publish Date: 2019-10-20

Abstract

Abstract

In lots of data based prediction or modeling applications, uncertainties and/or noises in the observed data cannot be avoided. In such cases, it is more preferable and reasonable to provide linguistic (fuzzy) predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers. Linguistic dynamic system (LDS) provides a powerful tool for yielding linguistic (fuzzy) results. However, it is still difficult to construct LDS models from observed data. To solve this issue, this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas. Then, a hybrid learning method is proposed to construct the data-driven LDS model. The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method, then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules, and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets. The proposed approach is successfully applied to three real-world prediction applications which are: prediction of energy consumption of a building, forecasting of the traffic flow, and prediction of the wind speed. Simulation results show that the uncertainties in the data can be effectively captured by the linguistic (fuzzy) estimates. It can also be extended to some other prediction or modeling problems, in which observed data have high levels of uncertainties.
- Fuzzy set,
- least square method,
- linguistic dynamic system (LDS),
- multi-objective optimization

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References

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A simplified linguistic dynamic system (LDS) which has closed-form expression is presented.
A hybrid learning method is proposed to construct the data-driven LDS model.
The LDS is applied to the linguistic predictions of the energy consumption, traffic flow and wind speed.
The proposed approach is easy to implement because it requires minimal human intervention.

A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems

doi: 10.1109/JAS.2019.1911543

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通讯作者: 陈斌, bchen63@163.com

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