Modelling and prediction of the MXNUSD exchange rate using interval singleton type-2 fuzzy logic systems

被引：3
|
作者：
de los Angeles Hernandez-M., Maria
Mendez, Gerardo M.
机构：
来源：
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | 2007年 / 2卷 / 01期
关键词：
D O I：
10.1109/MCI.2007.357189
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An interval type-2 frizzy logic systems (IT2 SFLS) using hybrid recursive least-squared method-back-propagation (RLS-BP) training method was tested and compared for forecasting the daily exchange rate between Mexican Peso and US Dollar (MXNUSD). The results showed that the IT2 SFLS forecaster using ULS-BP hybrid learning provided the best performance, and the base line T1 SFLS provided the worst performance. It is possible to directly use the daily data of exchange rate to train an IT2 SFLS to predict MXNUSD exchange rate one day in advance. It was observed that IT2 SFLS forecasters efficiently managed the uncertainties presented in the raw historical data. While, the performance evaluation for learning methods is based on root mean-squared error (RMSE) benchmarking criteria.
引用
收藏
页码：5 / 8
页数：4
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