Deep inference: A convolutional neural networks method for parameter recovery of the fractional dynamics
عنوان مقاله: Deep inference: A convolutional neural networks method for parameter recovery of the fractional dynamics
شناسه ملی مقاله: JR_IJNAA-12-1_016
منتشر شده در در سال 1400
شناسه ملی مقاله: JR_IJNAA-12-1_016
منتشر شده در در سال 1400
مشخصات نویسندگان مقاله:
- - - Faculty of Sciences, Imam Ali University, Tehran, Iran
- - - Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
- - - Faculty of Engineering, Imam Ali University, Tehran Iran
- - - Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
خلاصه مقاله:
- - - Faculty of Sciences, Imam Ali University, Tehran, Iran
- - - Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
- - - Faculty of Engineering, Imam Ali University, Tehran Iran
- - - Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti 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.
کلمات کلیدی: Convolutional Neural Network, Parameter estimation, Fractional Dynamics, Data driven discove
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1561296/