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首页> 外文期刊>International Journal of Computers & Applications >EFFICIENT TRAINING OF NEURAL NETWORKS USING OPTICAL BACKPROPAGATION WITH MOMENTUM FACTOR
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EFFICIENT TRAINING OF NEURAL NETWORKS USING OPTICAL BACKPROPAGATION WITH MOMENTUM FACTOR

机译：利用动量因子进行光学反向传播对神经网络进行有效训练

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摘要

Backpropagation (BP) algorithm [1], which is commonly used in training multilayer neural networks might take long time to converge in many cases. However, momentum is a standard method that is used to speed up convergence and escape from local minima. This paper presents a new technique called, an optical backpropagation algorithm (OBP) [2] with momentum factor that tries to speed up the training process. This has been demonstrated through a comparison analysis between the proposed one and similar one in the literature such as BP, BPM, Quickprop and Delta-bar-Delta. The efficiency of the proposed algorithm is shown through experiments on two training problems: XOR, and character recognition. The efficiency results for the proposed algorithm show its superiority over the existing ones. Therefore, the promising results that obtained may be used in different real areas of classifications.
机译：在许多情况下，通常用于训练多层神经网络的反向传播（BP）算法[1]可能需要很长时间才能收敛。但是，动量是一种标准方法，可用于加速收敛并避免局部极小值。本文提出了一种新技术，称为光反向传播算法（OBP）[2]，该算法具有动量因子，旨在加快训练过程。通过对建议的一个和类似文献（例如BP，BPM，Quickprop和Delta-bar-Delta）之间的比较分析，已证明了这一点。通过对两个训练问题的实验证明了所提算法的有效性：异或和字符识别。所提算法的效率结果表明其优于现有算法。因此，获得的有希望的结果可以用于不同的实际分类领域。

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