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Type-2 Fuzzy Weight Adjustment for Backpropagation in Prediction Time Series and Pattern Recognition

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Soft Computing Applications in Optimization, Control, and Recognition

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 294))

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Abstract

In this paper a genetic algorithm is used to optimize the three neural networks in an ensemble model. Genetic algorithms are also used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the proposed learning method architecture and the adaptation of 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 an ensemble neural network of three neural networks and average integration to obtain the final result is presented. The proposed approach is applied to a case of time series prediction and to pattern recognition.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, México

    Fernando Gaxiola, Patricia Melin & Fevrier Valdez

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  1. Fernando Gaxiola
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  2. Patricia Melin
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  3. Fevrier Valdez
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Correspondence to Fernando Gaxiola .

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Editors and Affiliations

  1. , Graduate Program of Computer Science, Tijuana Institute of Technology, Calzada Tecnologico s/n, Tijuana, 22379, Mexico

    Patricia Melin

  2. , Graduate Program of Computer Science, Tijuana Institute of Technology, Calzada Tecnologico s/n, Tijuana, 22379, Mexico

    Oscar Castillo

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Gaxiola, F., Melin, P., Valdez, F. (2013). Type-2 Fuzzy Weight Adjustment for Backpropagation in Prediction Time Series and Pattern Recognition. In: Melin, P., Castillo, O. (eds) Soft Computing Applications in Optimization, Control, and Recognition. Studies in Fuzziness and Soft Computing, vol 294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35323-9_8

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  • DOI: https://doi.org/10.1007/978-3-642-35323-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35322-2

  • Online ISBN: 978-3-642-35323-9

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