Deep inference: A convolutional neural networks method for parameter recovery of the fractional dynamics

Document Type : Research Paper


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


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.


Volume 12, Issue 1
April 2021
Pages 189-201
  • Receive Date: 05 October 2020
  • Revise Date: 21 December 2020
  • Accept Date: 26 December 2020
  • First Publish Date: 26 December 2020