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A Fast Identification Algorithm with Skewness Noises under Box-Cox Transformation-Based Annealing Robust Fuzzy Neural Networks

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Next Wave in Robotics (FIRA 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 212))

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Abstract

This paper proposes Box-Cox transformation-based annealing robust fuzzy neural networks (ARFNNs) that can be used effectively for function approximated problem with skewness noises. In order to overcome the skewness noises problem, the Box-Cox transformation that its object is usually to make residuals more homogeneous in regression, or transform data to be normally distributed has been added to the annealing robust fuzzy neural networks. That is, the proposed approach uses Box-Cox transformation for skewness noises problem and support vector regression (SVR) for the number of rule in the simplified fuzzy inference systems. After the initialization, an annealing robust learning algorithm (ARLA) is then applied to adjust the parameters of the Box-Cox transformation-based annealing robust fuzzy neural networks. Simulation results show that the proposed approach has a fast convergent speed and more generalization capability for the function approximated problem with skewness noises.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan

    Pi-Yun Chen

  2. Department of Automation Engineering, Nan-Kai University of Technology, Nantou, Taiwan

    Yu-Yi Fu

  3. Department of Computer Science and Information Engineering, National Formosa University, Yunlin, Taiwan

    Jin-Tsong Jeng

  4. Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Kuo-Lan Su

Authors
  1. Pi-Yun Chen
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  2. Yu-Yi Fu
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  3. Jin-Tsong Jeng
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  4. Kuo-Lan Su
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, National Cheng Kung University, No. 1, University Road, 701, Tainan City, Taiwan

    Tzuu-Hseng S. Li

  2. National Kaohsiung First University of Science and Technology, 2 Juoyue Road, 40227, Taichung, Taiwan

    Kuo-Yang Tu

  3. Department of Electrical Engineering, Nationan Chung Hsing University, No. 250, Kuo-Kuang Road, 40227, Taichung, Taiwan

    Ching-Chih Tsai

  4. Department of Applied Electronics Technology, National Taiwan Normal University, 162 He-Ping East Road, Section 1, 10610, Taipei, Taiwan

    Chen-Chien Hsu

  5. National Kaohsiung First University of Science and Technology, 2 Juoyue Road, Nantsu District, 811, Kaohsiung City, Taiwan

    Chien-Cheng Tseng

  6. National University of Singapore, Singapore

    Prahlad Vadakkepat

  7. Department of Computer Science,, University of Manitoba, R3T 2N2, Winnipeg, Manitoba, Canada

    Jacky Baltes

  8. Department of Computer Science, University of Manitoba, R3T 2N2, Winnipeg, Manitoba, Canada

    John Anderson

  9. Tamkang University, Taipei, Taiwan

    Ching-Chang Wong

  10. Technische Universität Dortmund, Dortmund, Germany

    Norbert Jesse

  11. Department of Electrical Engineering, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., 106, Taipei, Taiwan

    Chung-Hsien Kuo

  12. Institute of System Information and Control, National Kaohsiung First University of Science and Technology, 2 Juoyue Road, Nantsu District, 811, Kaohsiung City, Taiwan

    Haw-Ching Yang

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© 2011 Springer-Verlag Berlin Heidelberg

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Chen, PY., Fu, YY., Jeng, JT., Su, KL. (2011). A Fast Identification Algorithm with Skewness Noises under Box-Cox Transformation-Based Annealing Robust Fuzzy Neural Networks. In: Li, TH.S., et al. Next Wave in Robotics. FIRA 2011. Communications in Computer and Information Science, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23147-6_24

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  • DOI: https://doi.org/10.1007/978-3-642-23147-6_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23146-9

  • Online ISBN: 978-3-642-23147-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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