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Ensemble Neural Network with Type-2 Fuzzy Weights Using Response Integration for Time Series Prediction

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

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 361))

Abstract

In this paper an ensemble of three neural networks with type-2 fuzzy weights is proposed. One neural network uses type-2 fuzzy inference systems with Gaussian membership functions for obtain the fuzzy weights; the second neural network uses type-2 fuzzy inference systems with triangular membership functions; and the third neural network uses type-2 fuzzy inference systems with triangular membership functions with uncertainty in the standard deviation. Average integration and type-2 fuzzy integrator are used for the results of the ensemble neural network. The proposed approach is applied to a case of time series prediction, specifically in the Mackey-Glass time series.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Fernando Gaxiola, Patricia Melin & Fevrier Valdez

  2. UABC University, Tijuana, Mexico

    Juan R. Castro

Authors
  1. Fernando Gaxiola
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  2. Patricia Melin
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  3. Fevrier Valdez
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  4. Juan R. Castro
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Corresponding author

Correspondence to Fernando Gaxiola .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  2. Machine Intelligence Institute, Iona College, New Rochelle, New York, USA

    Ronald R. Yager

  3. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

  5. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

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Gaxiola, F., Melin, P., Valdez, F., Castro, J.R. (2018). Ensemble Neural Network with Type-2 Fuzzy Weights Using Response Integration for Time Series Prediction. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-75408-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75407-9

  • Online ISBN: 978-3-319-75408-6

  • eBook Packages: EngineeringEngineering (R0)

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