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

• Main Authors: Bingi, K., Devan, P.A.M., Hussin, F.A.
• Format: Conference or Workshop Item
• 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/

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.