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Ensemble Neural Network with Type-1 and Type-2 Fuzzy Integration for Time Series Prediction and Its Optimization with PSO

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Imprecision and Uncertainty in Information Representation and Processing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 332))

Abstract

This paper describes the design of ensemble neural networks using Particle Swarm Optimization (PSO) for time series prediction with Type-1 and Type-2 Fuzzy Integration. The time series that is being considered in this work is the Mackey-Glass benchmark time series. Simulation results show that the ensemble approach produces good prediction of the Mackey-Glass time series.

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Acknowledgments

We would like to express our gratitude to the CONACYT, 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, Mexico

    Patricia Melin, Martha Pulido & Oscar Castillo

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

Correspondence to Patricia Melin .

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Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg, Lancaster, United Kingdom

    Plamen Angelov

  2. Intelligent Systems Laboratory, Prof. Assen Zlatarov University, Bourgas, Bulgaria

    Sotir Sotirov

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Melin, P., Pulido, M., Castillo, O. (2016). Ensemble Neural Network with Type-1 and Type-2 Fuzzy Integration for Time Series Prediction and Its Optimization with PSO. In: Angelov, P., Sotirov, S. (eds) Imprecision and Uncertainty in Information Representation and Processing. Studies in Fuzziness and Soft Computing, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-319-26302-1_22

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  • DOI: https://doi.org/10.1007/978-3-319-26302-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26301-4

  • Online ISBN: 978-3-319-26302-1

  • eBook Packages: EngineeringEngineering (R0)

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