The Trend-Fuzzy-Granulation-Based Adaptive Fuzzy Cognitive Map for Long-Term Time Series Forecasting-Reference-Cited by-同舟云学术

The Trend-Fuzzy-Granulation-Based Adaptive Fuzzy Cognitive Map for Long-Term Time Series Forecasting

Published:2022-12 Issue:12 Volume:30 Page:5166-5180
ISSN:1063-6706
Container-title:IEEE Transactions on Fuzzy Systems
language:
Short-container-title:IEEE Trans. Fuzzy Syst.

Author:

Wang Yihan¹^ORCID,Yu Fusheng²^ORCID,Homenda Wladyslaw³^ORCID,Pedrycz Witold⁴^ORCID,Tang Yuqing¹^ORCID,Jastrzebska Agnieszka³^ORCID,Li Fang⁵

Affiliation:

1. School of Mathematical Sciences, Beijing Normal University, Beijing, China

2. School of Mathematics Sciences and Statistics, Minnan Normal University, Zhangzhou, China

3. Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

5. Department of Mathematics, College of Arts and Sciences, Shanghai Maritime University, Shanghai, China

Funder

National Natural Science Foundation of China

Narodowe Centrum Nauki

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/91/9967417/09763011.pdf?arnumber=9763011

Reference40 articles.

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3. The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy

4. Learning fuzzy cognitive maps by a hybrid method using nonlinear Hebbian learning and extended great deluge;ren;Proc IEEE 23rd Conf Midwest Artif Intell Cogn Sci,2008

5. An Integrated Framework for Learning Fuzzy Cognitive Map using RCGA and NHL Algorithm

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