Forecasting time series with a logarithmic model for the Polynomial Artificial Neural Networks
J. C. Luna-Sanchez, Eduardo Gomez-Ramirez, K. Najim, Enso Ikonen · 2011
The adaptation made for the Polynomial Artificial Neural Networks (PANN) using not only integer exponentials but also fractional exponentials, have shown evidence of its better performance, especially, when it works with non-linear and chaotic time series. In this paper we show the comparison of the PANN improved model of fractional exponentials with a new logarithmic model. We show that this new model have even better performance than the last PANN improved model.