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Fuzzy Model Identification and Self Learning with Smooth Compositions

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Abstract

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.

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Funding

This publication was supported in part by project MINECO, TEC2017-88048-C2-2-R.

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Authors and Affiliations

  1. Universidad Carlos III, Madrid, Spain

    Ebrahim Navid Sadjadi, Jesus Garcia & Jose Manuel Molina Lopez

  2. Department of Applied Mathematics, University of Science and Technology of Mazandaran, Behshahr, Iran

    Akbar Hashemi Borzabadi

  3. Department of Electrical Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran

    Monireh Asadi Abchouyeh

Authors
  1. Ebrahim Navid Sadjadi
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  2. Jesus Garcia
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  3. Jose Manuel Molina Lopez
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  4. Akbar Hashemi Borzabadi
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  5. Monireh Asadi Abchouyeh
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Corresponding author

Correspondence to Ebrahim Navid Sadjadi.

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The authors declare that there exists no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Sadjadi, E.N., Garcia, J., Molina Lopez, J.M. et al. Fuzzy Model Identification and Self Learning with Smooth Compositions. Int. J. Fuzzy Syst. 21, 2679–2693 (2019). https://doi.org/10.1007/s40815-019-00725-8

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  • DOI: https://doi.org/10.1007/s40815-019-00725-8

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