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 - <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>