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Fuzzy Modeling from Black-Box Data with Deep Learning Techniques

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10261))

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Abstract

Deep learning techniques have been successfully used for pattern classification. These advantage methods are still not applied in fuzzy modeling. In this paper, a novel data-driven fuzzy modeling approach is proposed. The deep learning methods is applied to learn the probability properties of input and output pairs. We propose special unsupervised learning methods for these two deep learning models with input data. The fuzzy rules are extracted from these properties. These deep learning based fuzzy modeling algorithms are validated with three benchmark examples.

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Acknowledgments

This paper is supported by the National Council of Science and Technology of Mexico (CONACYT), under the project Frontiers of Science (Grant No. 65).

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Authors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, Mexico City, Mexico

    Erick de la Rosa & Wen Yu

  2. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Humberto Sossa

Authors
  1. Erick de la Rosa
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  2. Wen Yu
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  3. Humberto Sossa
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Corresponding author

Correspondence to Wen Yu .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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de la Rosa, E., Yu, W., Sossa, H. (2017). Fuzzy Modeling from Black-Box Data with Deep Learning Techniques. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_36

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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