@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 = {<p style="text-align: justify;">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.</p>},
issn = {2617-8710},
doi = {https://doi.org/10.4208/ijnam2023-1031},
url = {http://global-sci.org/intro/article_detail/ijnam/22009.html}
}