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Chaotic Time Series Forecasting Using Higher Order Neural Networks
2016
International Journal on Advanced Science, Engineering and Information Technology
This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of
doi:10.18517/ijaseit.6.5.958
fatcat:uso5ldwbyran5altncy33gbpau