Modified Backpropagation with Added White Gaussian Noise in Weighted Sum for Convergence Improvement

The impact of the noise injection in backpropagation is studied since last two decades. In this manuscript, a brief review is done on noise injection techniques used in the backpropagation. Here, the modified backpropagation algorithm is proposed, in which the white Gaussian noise is added in the we...

Full description

Saved in:
Bibliographic Details
Published in:Procedia computer science 2018, Vol.143, p.309-316
Main Authors: Sapkal, Ashwini, Kulkarni, U V
Format: Article
Language:English
Subjects:
Backpropagation
Convergence
White Gaussian noise
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The impact of the noise injection in backpropagation is studied since last two decades. In this manuscript, a brief review is done on noise injection techniques used in the backpropagation. Here, the modified backpropagation algorithm is proposed, in which the white Gaussian noise is added in the weighted sum entity of the backpropagation. The experimentation is carried out on different standard benchmark problems such as 2 bit parity and iris dataset. It is observed that the proposed modified backpropagation requires less number of epochs as compared to the standard backpropagation for the convergence when applied on 2 bit parity and Iris dataset.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2018.10.401