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Neural Network with Type-2 Fuzzy Weights Adjustment for Pattern Recognition of the Human Iris Biometrics

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Advances in Computational Intelligence (MICAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7630))

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Abstract

In this paper a neural network learning method with type-2 fuzzy weight adjustment is proposed. 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 research of recent methods that handle weight adaptation and especially the use of fuzzy weights. In this work an ensemble neural network of three neural networks and the use of average integration to obtain the final result is presented. The proposed approach is applied to a case of time series prediction to illustrate the advantage of using type-2 fuzzy weights.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, México

    Fernando Gaxiola, Patricia Melin, Fevrier Valdez & Oscar Castillo

Authors
  1. Fernando Gaxiola
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  2. Patricia Melin
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  3. Fevrier Valdez
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  4. Oscar Castillo
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Editor information

Editors and Affiliations

  1. Mexican Petroleum Institute, Eje Central Lazaro Cardenas Norte, 152, Col. San Bartolo Atepehuacan, CP 07730, México D.F., Mexico

    Ildar Batyrshin

  2. Tecnológico de Monterrey, Campus Estado de México, Carretera Lago de Guadalupe Km 3.5, CP 52926, Atizapán de Zaragoza, Estado de México, Mexico

    Miguel González Mendoza

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Gaxiola, F., Melin, P., Valdez, F., Castillo, O. (2013). Neural Network with Type-2 Fuzzy Weights Adjustment for Pattern Recognition of the Human Iris Biometrics. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_23

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  • DOI: https://doi.org/10.1007/978-3-642-37798-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37797-6

  • Online ISBN: 978-3-642-37798-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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