Autor:

Bingi, Kishore ; Prusty, Rajanarayan B.

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

Forecasting models for chaotic fractional-order oscillators using neural networks

Tytuł publikacji grupowej:

AMCS, volume 31 (2021)

Temat i słowa kluczowe:

chaotic oscillators ; data-driven forecasting ; fractional-order systems ; model-free analysis ; neural networks ; time-series prediction

Abstract:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2021

Typ zasobu:

artykuł

DOI:

10.34768/amcs-2021-0026

Strony:

387-398

Źródło:

AMCS, volume 31, number 3 (2021) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego