Fractional-Order Deep Backpropagation Neural Network
Joint Authors
Bao, Chunhui
Pu, Yifei
Zhang, Yi
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In recent years, the research of artificial neural networks based on fractional calculus has attracted much attention.
In this paper, we proposed a fractional-order deep backpropagation (BP) neural network model with L2 regularization.
The proposed network was optimized by the fractional gradient descent method with Caputo derivative.
We also illustrated the necessary conditions for the convergence of the proposed network.
The influence of L2 regularization on the convergence was analyzed with the fractional-order variational method.
The experiments have been performed on the MNIST dataset to demonstrate that the proposed network was deterministically convergent and can effectively avoid overfitting.
American Psychological Association (APA)
Bao, Chunhui& Pu, Yifei& Zhang, Yi. 2018. Fractional-Order Deep Backpropagation Neural Network. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130831
Modern Language Association (MLA)
Bao, Chunhui…[et al.]. Fractional-Order Deep Backpropagation Neural Network. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1130831
American Medical Association (AMA)
Bao, Chunhui& Pu, Yifei& Zhang, Yi. Fractional-Order Deep Backpropagation Neural Network. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130831
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130831