Chaotic time series forecasting using locally quadratic fuzzy neural models
FS'08 Proceedings of the 9th WSEAS International Conference on Fuzzy Systems
Fuzzy subsethood for fuzzy sets of type-2 and generalized type-n
IEEE Transactions on Fuzzy Systems
α-plane representation for type-2 fuzzy sets: theory and applications
IEEE Transactions on Fuzzy Systems
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An IT2 SFLS using hybrid RLS-BP training method was tested and compared for forecasting the daily exchange rate between Mexican Peso and U.S. Dollar (MXNUSD). The results showed that the IT2 SFLS forecaster using RLS-BP hybrid learning provided the best performance, and the base line T1 SFLS provided the worst performance. We conclude, therefore, that it is possible to directly use the daily data of exchange rate to train an IT2 SFLS, in order 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