On the Identification and Generation of Discrete-Time Chaotic Systems with Recurrent Neural Networks

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We address the identification and generation of the discrete-time chaotic system (DTCS) with a two-layered recurrent neural network (RNN). First, we propose an identification procedure of the DTCS in which the RNN is required to have less layers than in the conventional procedures. Next, based on Li-Yorke theorem, we propose a generation procedure which enables us to predict a range of chaotic behavior of the DTCS in advance. Simulation results demonstrate that the proposed identification procedure, employing the Levenberg-Marquardt algorithm and a two-layered RNN, requires lower computational complexity than the conventional identification procedures at comparable performance.
Publisher
SPRINGER SINGAPORE PTE LTD
Issue Date
2019-07
Language
English
Article Type
Article
Citation

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, v.14, no.4, pp.1699 - 1706

ISSN
1975-0102
DOI
10.1007/s42835-019-00103-2
URI
http://hdl.handle.net/10203/263885
Appears in Collection
EE-Journal Papers(저널논문)
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