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Volume 20, Issue 5
Fractional Order Learning Methods for Nonlinear System Identification Based on Fuzzy Neural Network

Jie Ding, Sen Xu & Zhijie Li

DOI: 10.4208/ijnam2023-1031

Int. J. Numer. Anal. Mod., 20 (2023), pp. 709-723.

Published online: 2023-09

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  • Abstract

This paper focuses on neural network-based learning methods for identifying nonlinear dynamic systems. The Takagi-Sugeno (T-S) fuzzy model is introduced to represent nonlinear systems in a linear way. Fractional calculus is integrated to minimize the cost function, yielding a fractional-order learning algorithm that can derive optimal parameters in the T-S fuzzy model. The proposed algorithm is evaluated by comparing it with an integer-order method for identifying numerical nonlinear systems and a water quality system. Both evaluations demonstrate that the proposed algorithm can effectively reduce errors and improve model accuracy.

  • Keywords

Fractional calculus, T-S fuzzy neural network, gradient descent method, nonlinear systems.

  • AMS Subject Headings

65-XX

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-20-709, author = {Ding , JieXu , Sen and Li , Zhijie}, title = {Fractional Order Learning Methods for Nonlinear System Identification Based on Fuzzy Neural Network}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2023}, volume = {20}, number = {5}, pages = {709--723}, abstract = {

This paper focuses on neural network-based learning methods for identifying nonlinear dynamic systems. The Takagi-Sugeno (T-S) fuzzy model is introduced to represent nonlinear systems in a linear way. Fractional calculus is integrated to minimize the cost function, yielding a fractional-order learning algorithm that can derive optimal parameters in the T-S fuzzy model. The proposed algorithm is evaluated by comparing it with an integer-order method for identifying numerical nonlinear systems and a water quality system. Both evaluations demonstrate that the proposed algorithm can effectively reduce errors and improve model accuracy.

}, issn = {2617-8710}, doi = {https://doi.org/10.4208/ijnam2023-1031}, url = {http://global-sci.org/intro/article_detail/ijnam/22009.html} }
TY - JOUR T1 - Fractional Order Learning Methods for Nonlinear System Identification Based on Fuzzy Neural Network AU - Ding , Jie AU - Xu , Sen AU - Li , Zhijie JO - International Journal of Numerical Analysis and Modeling VL - 5 SP - 709 EP - 723 PY - 2023 DA - 2023/09 SN - 20 DO - http://doi.org/10.4208/ijnam2023-1031 UR - https://global-sci.org/intro/article_detail/ijnam/22009.html KW - Fractional calculus, T-S fuzzy neural network, gradient descent method, nonlinear systems. AB -

This paper focuses on neural network-based learning methods for identifying nonlinear dynamic systems. The Takagi-Sugeno (T-S) fuzzy model is introduced to represent nonlinear systems in a linear way. Fractional calculus is integrated to minimize the cost function, yielding a fractional-order learning algorithm that can derive optimal parameters in the T-S fuzzy model. The proposed algorithm is evaluated by comparing it with an integer-order method for identifying numerical nonlinear systems and a water quality system. Both evaluations demonstrate that the proposed algorithm can effectively reduce errors and improve model accuracy.

Jie Ding, Sen Xu & Zhijie Li. (2023). Fractional Order Learning Methods for Nonlinear System Identification Based on Fuzzy Neural Network. International Journal of Numerical Analysis and Modeling. 20 (5). 709-723. doi:10.4208/ijnam2023-1031
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