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

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.

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 6; Conference of 2021 Australian and New Zealand Control Conference, ANZCC 2021 ; Conference Date: 25 November 2021 Through 26 November 2021; Conference Code:175356
Uncontrolled Keywords: Chaotic systems; Mean square error; Recurrent neural networks, Adams-Bashforth methods; Attractor reconstruction; Chaos in fractional-order system; Chaotic attractors; Forecasting models; Fractional order; Fractional-order systems; RNN; State-variables; System state, Forecasting
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:30
Last Modified: 10 Nov 2023 03:30
URI: https://khub.utp.edu.my/scholars/id/eprint/15513

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