Abstract
This paper presents a novel application of the interval type-1 non-singleton type-2 fuzzy logic system (FLS) for one step ahead prediction of the daily exchange rate between Mexican Peso and US Dollar (MXNUSD) using the recursive least-squared (RLS)-back-propagation (BP) hybrid learning method. Experiments show that the exchange rate is predictable. A non-singleton type-1 FLS and an interval type-1 non-singleton type-2 FLS, both using only BP learning method, are used as a benchmarking systems to compare the results of the hybrid interval type-1 non-singleton type-2 FLS (RLS-BP) forecaster.
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Mendez, G.M., Hernandez, A. (2009). Prediction of the MXNUSD Exchange Rate Using Hybrid IT2 FLS Forecaster. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. Studies in Computational Intelligence, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04516-5_10
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DOI: https://doi.org/10.1007/978-3-642-04516-5_10
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