文章基本信息

标题：Optimasi Learning Rate Neural Network Backpropagation Dengan Search Direction Conjugate Gradient Pada Electrocardiogram
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作者：Azwar Riza Habibi ; Vivi Aida Fitria ; Lukman Hakim 等
期刊名称：Numerical: Jurnal Matematika dan Pendidikan Matematika
印刷版ISSN：2580-3573
电子版ISSN：2580-2437
出版年度：2019
页码：131-137
DOI：10.25217/numerical.v3i2.603
出版社：Institut Agama Islam Ma'arif NU (IAIMNU) Metro Lampung
摘要：This paper develops a Neural network (NN) using conjugate gradient (CG). The modification of this method is in defining the direction of linear search. The conjugate gradient method has several methods to determine the steep size such as the Fletcher-Reeves, Dixon, Polak-Ribere, Hestene Steifel, and Dai-Yuan methods by using discrete electrocardiogram data. Conjugate gradients are used to update learning rates on neural networks by using different steep sizes. While the gradient search direction is used to update the weight on the NN. The results show that using Polak-Ribere get an optimal error, but the direction of the weighting search on NN widens and causes epoch on NN training is getting longer. But Hestene Steifel, and Dai-Yua could not find the gradient search direction so they could not update the weights and cause errors and epochs to infinity.
关键词：Conjugate Gradient;