Forecasting time series with a new architecture for polynomial artificial neural network

被引：19
|
作者：
Gomez-Ramirez, E.
Najim, K.
Ikonen, E.
机构：
[1] Univ Salle, Lab Invest & Desarrollo Tecnol Avanzada Lidetea, Mexico City 06140, DF, Mexico
[2] ENSIACET, Proc Control Lab, F-31077 Toulouse 4, France
[3] Univ Oulu, Dept Proc & Environm Engn, Syst Engn Lab, Infotech Oulu, FIN-90014 Oulu, Linnanmaa, Finland
来源：
APPLIED SOFT COMPUTING | 2007年 / 7卷 / 04期
关键词：
forecasting; chaotic time series; polynomial artificial neural network;
D O I：
10.1016/j.asoc.2006.01.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Polynomial artificial neural networks (PANN) have been shown to be powerful for forecasting nonlinear time series. The training time is small compared to the time used by other algorithms of artificial neural networks and the capacity to compute relations between the inputs and outputs represented by every term of the polynomial. In this paper a new structure of polynomial is presented that improves the performance of this type of network considering only non-integers exponents. The architecture adaptation uses genetic algorithm (GA) to find the optimal architecture for every example. Some examples of sunspots and chaotic time series are presented. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：1209 / 1216
页数：8
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