Predictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data

被引：0

作者：

Garibo-i-Orts, Oscar ^{[1
,2
]}

Lizama, Carlos ^{[3
]}

Akgul, Ali ^{[4
,5
]}

Conejero, J. Alberto ^{[1
]}

机构：

[1] Inst Univ Matemat Pura & Aplicada, Univ Politecn Valencia, Valencia, Spain

[2] Valencian Int Univ VIU, GRID Grp Invest Ciencia Datos, Carrer Pintor Sorolla 21, Valencia 46002, Spain

[3] Univ Santiago de Chile, Dept Matemat & Ciencia Comp, Las Sophoras 173, Santiago, Chile

[4] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon

[5] Siirt Univ, Art & Sci Fac, Dept Math, TR-56100 Siirt, Turkiye

来源：

CHINESE JOURNAL OF PHYSICS | 2024年 / 89卷

关键词：

Dynamical systems; Discrete fractional calculus; Wu-Baleanu model; Logistic map; Convolutional neural networks; LSTM networks; SYSTEMS;

D O I：

10.1016/j.cjph.2024.04.010

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We study the accuracy of machine learning methods for inferring the parameters of noisy fractional Wu-Baleanu trajectories with some missing initial terms. Our model is based on a combination of convolutional and recurrent neural networks (LSTM), which permits the extraction of characteristics from trajectories while preserving time dependency. We show that these approach exhibit good accuracy results despite the poor quality of the data.

引用

页码：1276 / 1285

页数：10

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