Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm

Data Science: Journal of Computing and Applied Informatics

View Publication Info
 
 
Field Value
 
Title Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm
 
Creator Singarimbun, Roy Nuary
 
Subject Gradient Descent Backpropagation
Adaptive Moment Estimation
Minimize Square Error
 
Description Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient descent algorithm. Adaptive Estimation moment can speed up the training and achieve the level of acceleration to get linear. ADAM can adapt to changes in the system, and can optimize many parameters with a low calculation. The results of the study indicate that the performance of adaptive moment estimation can minimize the squared error in the output of neural networks.
 
Publisher Talenta Publisher
 
Date 2020-02-05
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://talenta.usu.ac.id/JoCAI/article/view/1160
10.32734/jocai.v4.i1-1160
 
Source Data Science: Journal of Computing and Applied Informatics; Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI); 27-46
2580-829X
2580-6769
 
Language eng
 
Relation https://talenta.usu.ac.id/JoCAI/article/view/1160/2720
 
Rights Copyright (c) 2020 Data Science: Journal of Computing and Applied Informatics
https://creativecommons.org/licenses/by-nc-nd/4.0
 

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