基于层次结构随机自动机的带动量反向传播神经网络学习算法研究

N. Baba, Ken Sato
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引用次数: 2

摘要

带动量的反向传播(BP)方法被广泛应用于人工神经网络的各种模式分类问题。然而,该方法的一个重要局限性是其学习性能在很大程度上取决于动量因子和步长值的选择。本文证明了层次结构随机自动机可以用于寻找具有动量的BP方法中所涉及的参数的适当值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A consideration on the learning algorithm of neural network-utilization of the hierarchical structure stochastic automata for the backpropagation method with momentum
Backpropagation (BP) method with momentum has often been applied to adapt artificial neural networks for various pattern classification problems. However, an important limitation of this method is that its learning performance depends heavily upon the selection of the values of momentum factor and step size. In this paper, it is shown that the hierarchical structure stochastic automata can be used for finding appropriate values of the parameters involved in the BP method with momentum.
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