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Design of Ensemble Neural Networks for Predicting the US Dollar/MX Time Series with Particle Swarm Optimization

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Recent Developments and New Direction in Soft-Computing Foundations and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 342))

Abstract

This paper shows the use of particle swarm optimization (PSO) in the design of a neural network ensemble with type-1 and type-2 fuzzy integration of responses for time series prediction. The considered time series in this paper for testing the hybrid approach is the US/Dollar MX time series. Simulation results show that the hybrid ensemble approach, combining neural networks and fuzzy logic, produces good prediction of the dollar time series.

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Acknowledgments

We would like to express our gratitude to CONACYT, and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, B.C., Mexico

    Martha Pulido, Patricia Melin & Oscar Castillo

Authors
  1. Martha Pulido
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  2. Patricia Melin
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  3. Oscar Castillo
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Corresponding author

Correspondence to Martha Pulido .

Editor information

Editors and Affiliations

  1. University of California, Berkeley, USA

    Lotfi A. Zadeh

  2. Tech. of the Republic of Azerbaijan, Ministry of Communications Info., Baku, Azerbaijan

    Ali M. Abbasov

  3. Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

  4. Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  5. University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

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Pulido, M., Melin, P., Castillo, O. (2016). Design of Ensemble Neural Networks for Predicting the US Dollar/MX Time Series with Particle Swarm Optimization. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-32229-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32227-8

  • Online ISBN: 978-3-319-32229-2

  • eBook Packages: EngineeringEngineering (R0)

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