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Three-Layer Weighted Fuzzy Support Vector Regressions for Emotional Intention Understanding

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Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 926))

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Abstract

A three-layer weighted fuzzy support vector regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human-robot interaction. TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-means (AWKFCM) for data clustering, fuzzy support vector regressions (FSVR) for information understanding, and weighted fusion for intention understanding.

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Author information

Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, China

    Luefeng Chen

  2. School of Automation, China University of Geosciences, Wuhan, China

    Min Wu

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Tokyo Institute of Technology, Yokohama, Japan

    Kaoru Hirota

Authors
  1. Luefeng Chen
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  2. Min Wu
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  3. Witold Pedrycz
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  4. Kaoru Hirota
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Correspondence to Luefeng Chen .

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Chen, L., Wu, M., Pedrycz, W., Hirota, K. (2021). Three-Layer Weighted Fuzzy Support Vector Regressions for Emotional Intention Understanding. In: Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems. Studies in Computational Intelligence, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-61577-2_9

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