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Choquet Integral with Interval Type 2 Sugeno Measures as an Integration Method for Modular Neural Networks

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

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 342))

Abstract

In this paper, a new method for response integration, based on the Choquet integral with interval type 2 Sugeno measures, is presented. Type 1 and interval type 2 fuzzy systems for edge detection based on the Sobel and morphological gradient are used, which is a preprocessing system applied to the training data for better performance in the modular neural network. Fuzzy Sugeno measures are represented by an interval type 2 fuzzy system. The Choquet integral is used as a method to integrate the outputs of the modules of the modular neural networks (MNN). A database of faces was used to perform the preprocessing, the training, and the combination of information sources of the MNN.

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Acknowledgment

We are grateful to the MyDCI program of the Division of Graduate Studies and Research, UABC, Tijuana Institute of Technology and for the financial support provided by our sponsor CONACYT contract grant number: 189350.

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Authors and Affiliations

  1. Faculty of Chemical Sciences and Engineering Autonomous, University of Baja California, Tijuana, Mexico

    Gabriela E. Martínez, Olivia Mendoza & Juan R. Castro

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

    Patricia Melin & Oscar Castillo

Authors
  1. Gabriela E. Martínez
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  2. Olivia Mendoza
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  3. Juan R. Castro
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  4. Patricia Melin
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  5. Oscar Castillo
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Corresponding author

Correspondence to Gabriela E. Martínez .

Editor information

Editors and Affiliations

  1. University of California, Berkeley, USA

    Lotfi A. Zadeh

  2. Tech. of the Republic of Azerbaijan, Ministry of Communications Info., Baku, Azerbaijan

    Ali M. Abbasov

  3. Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

  4. Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  5. University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

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Martínez, G.E., Mendoza, O., Castro, J.R., Melin, P., Castillo, O. (2016). Choquet Integral with Interval Type 2 Sugeno Measures as an Integration Method for Modular Neural Networks. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-32229-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32227-8

  • Online ISBN: 978-3-319-32229-2

  • eBook Packages: EngineeringEngineering (R0)

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