FRACTIONAL ORDER LEARNING METHODS FOR NONLINEAR SYSTEM IDENTIFICATION BASED ON FUZZY NEURAL NETWORK

被引：0

作者：

Ding, Jie ^{[1
]}

Xu, Sen ^{[1
]}

Li, Zhijie ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Sch Automat & Artificial Intelligence, Nanjing 210023, Peoples R China

来源：

INTERNATIONAL JOURNAL OF NUMERICAL ANALYSIS AND MODELING | 2023年 / 20卷 / 05期

关键词：

Fractional calculus; T-S fuzzy neural network; gradient descent method; nonlinear systems; ALGORITHM; MODEL;

D O I：

10.4208/ijnam2023-1031

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页码：709 / 723

页数：15

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