Title :
Forecasting time series with a logarithmic model for the Polynomial Artificial Neural Networks
Author :
Luna-Sanchez, J.C. ; Gomez-Ramirez, E. ; Najim, K. ; Ikonen, E.
Author_Institution :
Intell. Syst. Group, La Salle Univ., Mexico City, Mexico
fDate :
July 31 2011-Aug. 5 2011
Abstract :
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.
Keywords :
forecasting theory; neural nets; time series; chaotic time series; fractional exponentials; integer exponentials; logarithmic model; nonlinear time series; polynomial artificial neural networks; time series forecasting; Artificial neural networks; Genetic algorithms; Mathematical model; Modeling; Polynomials; Time series analysis; Training;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033576
