Search
Abstract
In the paper, authors explore the possibility of using the recurrent neural networks (RNN) - Elman, GRU and LSTM - for an approximation of the solution of the fractional-orders differential equations. The RNN network parameters are estimated via optimisation with the second order L-BFGS algorithm. It is done based on data from four systems: simple first and second fractional order LTI systems, a system of fractional-order point kinetics and heat exchange in the nuclear reactor core and complex nonlinear system. The obtained result shows that the studied RNNs are very promising as approximators of the fractional-order systems. On the other hand, these approximations may be easily implemented in real digital control platforms.
Citations
-
3
CrossRef
-
0
Web of Science
-
3
Scopus
Authors (2)
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
- Copyright (2020 Springer Nature Switzerland AG)
Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- Artificial Intelligence and Soft Computing strony 215 - 230
- Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- Puchalski B., Rutkowski T.: Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks// Artificial Intelligence and Soft Computing/ : , 2020, s.215-230
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-030-61401-0_21
- Verified by:
- Gdańsk University of Technology
seen 106 times