Deep inference: A Convolutional Neural Networks Method for Parameter Recovery of the Fractional Dynamics

Document Type : Research Paper

Authors

1 Faculty of Sciences, Imam Ali University, Tehran, Iran

2 Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran

3 Faculty of Engineering, Imam Ali University, Tehran Iran

10.22075/ijnaa.2021.4757

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

International Journal of Nonlinear Analysis and Applications
Volume 12, Issue 1
Winter and Spring 2021
Pages 189-201

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