Deep inference: A convolutional neural networks method for parameter recovery of the fractional dynamics | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 16، دوره 12، شماره 1، مرداد 2021، صفحه 189-201 اصل مقاله (1.83 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.4757 | ||
| نویسندگان | ||
| Nader Biranvand* 1؛ Amir Hossein Hadian-Rasanan2؛ Ali Khalili3؛ Jamal Amani Rad2 | ||
| 1Faculty of Sciences, Imam Ali University, Tehran, Iran | ||
| 2Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran | ||
| 3Faculty of Engineering, Imam Ali University, Tehran Iran | ||
| چکیده | ||
| Parameter recovery of dynamical systems has attracted much attention in recent years. The proposed methods for this purpose can not be used in real-time applications. Besides, little works have been done on the parameter recovery of the fractional dynamics. Therefore, in this paper, a convolutional neural network is proposed for parameter recovery of the fractional dynamics. The presented network can also estimate the uncertainty of the parameter estimation and has perfect robustness for real-time applications. | ||
| کلیدواژهها | ||
| Convolutional neural network؛ Parameter estimation؛ Fractional Dynamics؛ Data driven discove | ||
| مراجع | ||
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