Forecasting models for chaotic fractional-order oscillators using neural networks

• Autorzy: Bingi Kishore , Prusty B Rajanarayan

Identyfikatory

DOI

10.34768/amcs-2021-0026

• Języki publikacji: EN

Abstrakty

EN

This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing’s, Van der Pol’s, Tamaševičius’s and Chua’s, using feedforward neural networks. The models predict a change in the state values which bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators. The improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model’s out-of-sample forecasting plots show the best tracking ability to approximate future state values.

Słowa kluczowe

EN

chaotic oscillators; data driven forecasting; fractional order system; model free analysis; neural network; time series prediction

PL

oscylator chaotyczny; układ rzędu ułamkowego; sieć neuronowa; prognozowanie szeregów czasowych

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 3
• Strony: 387--398
• Opis fizyczny: Bibliogr. 39 poz., rys., tab., wykr.

Twórcy

autor

Bingi Kishore

• School of Electrical Engineering, Vellore Institute of Technology, Tiruvalam Rd, Katpadi, Vellore, 632014, Tamil Nadu, India

autor

Prusty B Rajanarayan

• School of Electrical Engineering, Vellore Institute of Technology, Tiruvalam Rd, Katpadi, Vellore, 632014, Tamil Nadu, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).