一种新的基于共轭梯度和输出权优化的神经网络算法

Y. Li, Xun Cai, M. Li
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引用次数: 0

摘要

本文在三层全连接神经网络模型的基础上,将输出权优化算法(OWO)与共轭梯度算法(CG)相结合,提出了一种新的输出权优化-共轭梯度算法(OWO-CG)。每次的学习过程分为两步:第一步,使用共轭梯度优化方法计算学习因子,然后只修改输入层对隐藏层的权值;第二步,利用隐层单元的输出构造并求解线性方程,计算输出层的权值。实验结果表明,与梯度下降算法、共轭梯度算法和输出权值优化算法相比,新算法大大提高了训练速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Notice of RetractionA new neural network algorithm based on conjugate gradient and output weight optimization
On the foundation of the three layers fully connected neural network model, this paper proposed a new algorithm which called output weight optimization-conjugate gradient algorithm (OWO-CG) based on the combination of the output weight optimization algorithm (OWO) and conjugate gradient algorithm (CG). Every time of the learning process is divided into two steps: the first step, use conjugate gradient optimization method to calculate learning factor, and then only modify the weights of input layer to hidden layer; the second step, use the output of hidden layer units to construct and solve linear equations to calculate the weights of output layer. Experimental results show that the new algorithm has greatly improved the training speed compared to the gradient descent algorithms, conjugate gradient algorithm and output weight optimization.
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