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Extreme learning machine with fuzzy input and fuzzy output for fuzzy regression

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Abstract

It is practically and theoretically significant to approximate and simulate a system with fuzzy inputs and fuzzy outputs. This paper proposes a extreme learning machine (ELM)-based fuzzy regression model (\({{\rm FR}}_{{{\rm ELM}}}\)) in which both inputs and outputs are triangular fuzzy numbers. Algorithm for training \({{\rm FR}}_{{{\rm ELM}}}\) is designed, and its computational complexity is analyzed. Furthermore, the convergence and error estimation for \({{\rm FR}}_{{{\rm ELM}}}\) are discussed. Numerical simulations show that the proposed \({{\rm FR}}_{{{\rm ELM}}}\) can effectively approximate a fuzzy input and fuzzy output system.

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Authors and Affiliations

  1. College of Mathematics and Information Technology, Xingtai University, Xingtai, 054001, China

    Hai-tao Liu

  2. Modern Education Technology Center, Hebei Institute of Physical Education, Shijiazhuang, 050041, China

    Jing Wang

  3. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Yu-lin He

  4. Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan

    Rana Aamir Raza Ashfaq

Authors
  1. Hai-tao Liu
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  2. Jing Wang
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  3. Yu-lin He
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  4. Rana Aamir Raza Ashfaq
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Corresponding author

Correspondence to Yu-lin He.

Additional information

We thank three anonymous reviewers whose valuable comments and suggestions help us significantly improve this paper. This work is supported by China Postdoctoral Science Foundation (2015M572361), National Natural Science Foundations of China (61503252), and Hebei Province Science and Technology Support Program Project (13210351).

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Liu, Ht., Wang, J., He, Yl. et al. Extreme learning machine with fuzzy input and fuzzy output for fuzzy regression. Neural Comput & Applic 28, 3465–3476 (2017). https://doi.org/10.1007/s00521-016-2232-9

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