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Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning for the Dow-Jones Time Series

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Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 601))

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Abstract

In this paper, type-1 and type-2 fuzzy inferences systems are used to obtain the type-1 or type-2 fuzzy weights in the connections between the layers of a neural network. We use two type-1 or type-2 fuzzy systems that work in the backpropagation learning method with the type-1 or type-2 fuzzy weight adjustment. The mathematical analysis of the proposed learning method architecture and the adaptation of type-1 or type-2 fuzzy weights are presented. The proposed method is based on recent methods that handle weight adaptation and especially fuzzy weights. In this work neural networks with type-1 fuzzy weights or type-2 fuzzy weights are presented. The proposed approach is applied to the case of Dow-Jones time series prediction for evaluating its efficiency.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Fernando Gaxiola, Patricia Melin & Fevrier Valdez

Authors
  1. Fernando Gaxiola
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  2. Patricia Melin
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  3. Fevrier Valdez
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Corresponding author

Correspondence to Fevrier Valdez .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Gaxiola, F., Melin, P., Valdez, F. (2015). Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning for the Dow-Jones Time Series. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-17747-2_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-17747-2

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