Year: 2023
Author: Jie Ding, Sen Xu, Zhijie Li
International Journal of Numerical Analysis and Modeling, Vol. 20 (2023), Iss. 5 : pp. 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.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/ijnam2023-1031
International Journal of Numerical Analysis and Modeling, Vol. 20 (2023), Iss. 5 : pp. 709–723
Published online: 2023-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 15
Keywords: Fractional calculus T-S fuzzy neural network gradient descent method nonlinear systems.