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Hybrid IT2 NSFLS-1 Used to Predict the Uncertain MXNUSD Exchange Rate

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Book cover Hybrid Artificial Intelligence Systems (HAIS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5271))

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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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Authors and Affiliations

  1. Instituto Tecnologico de Nuevo Leon, Av. Eloy Cavazos #2001, CP 67170, Cd. Guadalupe, NL, México

    Gerardo M. Mendez & Angeles Hernandez

Authors
  1. Gerardo M. Mendez
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  2. Angeles Hernandez
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Ajith Abraham

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton,, T6G 2V4, Alberta, Canada

    Witold Pedrycz

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© 2008 Springer-Verlag Berlin Heidelberg

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Mendez, G.M., Hernandez, A. (2008). Hybrid IT2 NSFLS-1 Used to Predict the Uncertain MXNUSD Exchange Rate. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_71

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  • DOI: https://doi.org/10.1007/978-3-540-87656-4_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87655-7

  • Online ISBN: 978-3-540-87656-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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