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Optimization of Type-2 Fuzzy Integration in Modular Neural Networks Using an Evolutionary Method with Applications in Multimodal Biometry

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MICAI 2009: Advances in Artificial Intelligence (MICAI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5845))

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Abstract

We describe in this paper a new evolutionary method for the optimization of a modular neural network for multimodal biometry The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions and rules) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using type-1 and type-2 fuzzy inference systems.

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Author information

Authors and Affiliations

  1. School of Engineering UABC University, Tijuana, México

    Denisse Hidalgo & Guillerrno Licea

  2. Division of Graduate Studies Tijuana Institute of Technology, Tijuana, México

    Patricia Melin & Oscar Castillo

Authors
  1. Denisse Hidalgo
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  2. Patricia Melin
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  3. Guillerrno Licea
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  4. Oscar Castillo
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Matemáticas, AC. (CIMAT), Area de Computación, Calle Jalisco S/N, Mineral de Valenciana, Guanajuato, CP 36250, Guanajuato, México

    Arturo Hernández Aguirre

  2. Ciencias de la Computación, Tecnológico de Monterrey, Campus Estado de México, Carretera al lago de Guadalupe Km 3,5, Col. Margarita Maza de Juárez, Atizapán de Zaragoza,, CP 52926, Estado de México, México

    Raúl Monroy Borja

  3. Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE), Ciencias Computacionales, Luis Enrique Erro No. 1, Santa María Tonantzintla, CP 72840, Puebla, México

    Carlos Alberto Reyes Garciá

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Hidalgo, D., Melin, P., Licea, G., Castillo, O. (2009). Optimization of Type-2 Fuzzy Integration in Modular Neural Networks Using an Evolutionary Method with Applications in Multimodal Biometry. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_40

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  • DOI: https://doi.org/10.1007/978-3-642-05258-3_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05257-6

  • Online ISBN: 978-3-642-05258-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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