Title of article
Deep inference: A convolutional neural networks method for parameter recovery of the fractional dynamics
Author/Authors
Biranvand, Nader Faculty of Sciences - Imam Ali University, Tehran, Iran , Hadian-Rasanan, Amir Hossein Department of Cognitive Modeling - Institute for Cognitive and Brain Sciences - Shahid Beheshti University, Tehran, Iran , Khalili, Ali Faculty of Engineering - Imam Ali University, Tehran Iran , Amani Rad, Jamal Department of Cognitive Modeling - Institute for Cognitive and Brain Sciences - Shahid Beheshti University, Tehran, Iran
Pages
13
From page
189
To page
201
Abstract
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.
Keywords
Convolutional neural network , Parameter estimation , Fractional Dynamics , Data driven discove
Journal title
International Journal of Nonlinear Analysis and Applications
Serial Year
2021
Record number
2606867
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