Nonlinear system modeling using the takagi-sugeno fuzzy model and long-short term memory cells

 The data driven black-box or gray-box models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we combine the Takagi-Sugeno fuzzy model with long-short term memory cells to overcome these disadvanta...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2020-01, Vol.39 (3), p.4547-4556
Main Authors: Yu, Wen, Vega, Francisco
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Fuzzy logic
Fuzzy systems
Machine learning
Mathematical models
Neural networks
Nonlinear systems
Short term
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Summary: The data driven black-box or gray-box models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we combine the Takagi-Sugeno fuzzy model with long-short term memory cells to overcome these disadvantages. This novel model takes the advantages of the interpretability of the fuzzy system and the good approximation ability of the long-short term memory cell. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in order to observe the advantages of the proposal.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-200491