Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks

In this paper, a forecasting model using recur-rent neural networks (RNN) for reconstructing the chaotic fractional-order Tamaševi�ius system states has been developed. The attractiveness of the proposed model is in the developed relationships between inputs, which are state variables, and outputs...

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Main Authors: Bingi, K., Devan, P.A.M., Hussin, F.A.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29245 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123629486&doi=10.1109%2fANZCC53563.2021.9628225&partnerID=40&md5=f6d9bcd83b5dc265e08eb21e683b536b
http://eprints.utp.edu.my/29245/
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spelling utp-eprints.292452022-03-25T01:15:34Z Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks Bingi, K. Devan, P.A.M. Hussin, F.A. In this paper, a forecasting model using recur-rent neural networks (RNN) for reconstructing the chaotic fractional-order Tamaševi�ius system states has been developed. The attractiveness of the proposed model is in the developed relationships between inputs, which are state variables, and outputs, which are the change in state variables for accurate prediction. The results from the proposed model show the best prediction ability for all three output variables with the highest R2 and the least mean square errors. The proposed forecasting model also performs best in reconstructing all three system states with minimal mean square errors. © 2021 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123629486&doi=10.1109%2fANZCC53563.2021.9628225&partnerID=40&md5=f6d9bcd83b5dc265e08eb21e683b536b Bingi, K. and Devan, P.A.M. and Hussin, F.A. (2021) Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks. In: UNSPECIFIED. http://eprints.utp.edu.my/29245/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In this paper, a forecasting model using recur-rent neural networks (RNN) for reconstructing the chaotic fractional-order Tamaševi�ius system states has been developed. The attractiveness of the proposed model is in the developed relationships between inputs, which are state variables, and outputs, which are the change in state variables for accurate prediction. The results from the proposed model show the best prediction ability for all three output variables with the highest R2 and the least mean square errors. The proposed forecasting model also performs best in reconstructing all three system states with minimal mean square errors. © 2021 IEEE.
format Conference or Workshop Item
author Bingi, K.
Devan, P.A.M.
Hussin, F.A.
spellingShingle Bingi, K.
Devan, P.A.M.
Hussin, F.A.
Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks
author_sort Bingi, K.
title Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks
title_short Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks
title_full Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks
title_fullStr Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks
title_full_unstemmed Reconstruction of Chaotic Attractor for Fractional-order Tamaševi�ius System Using Recurrent Neural Networks
title_sort reconstruction of chaotic attractor for fractional-order tamaå¡eviä�ius system using recurrent neural networks
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123629486&doi=10.1109%2fANZCC53563.2021.9628225&partnerID=40&md5=f6d9bcd83b5dc265e08eb21e683b536b
http://eprints.utp.edu.my/29245/
_version_ 1741197211263303680
score 11.62408

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