Forecasting models for chaotic fractional-order oscillators using neural networks - Bingi, Kishore; Prusty, Rajanarayan B. (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: chaotic oscillators; data-driven forecasting; fractional-order systems; model-free analysis; neural networks; time-series prediction