NEURAL NETWORK METHOD FOR PARAMETER ESTIMATION OF FRACTIONAL DISCRETE-TIME UNIFIED SYSTEMS

被引：2
|
作者：
Wu, Zhi-Qiang [1 ]
Wu, Guo-Cheng [1 ]
Zhu, Wei [1 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Key Lab Intelligent Anal & Decis Complex Syst, Chongqing 400065, Peoples R China
来源：
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY | 2024年 / 32卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Fractional Unified System; Parameter Estimation; Neural Network; Deep Learning; CHAOTIC SYSTEMS; IDENTIFICATION;
D O I：
10.1142/S0218348X2450004X
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Data-driven learning of the fractional discrete-time unified system is studied in this paper. A neural network method is suggested in the parameter estimation of fractional discrete-time chaotic systems. An optimization problem is obtained and the famous Adam algorithm is employed to train the neural network's weights and parameters. The parameter estimation result is compared with that of the stepwise response sensitivity approach (SRSA). This paper provides a high accuracy method for parameter inverse problems. The method also can be applied to data-driven learning of other fractional chaotic systems.
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页数：8
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