工业字符读取器的神经网络结构

S. Hata, K. Seino, A. Yagisawa
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引用次数: 0

摘要

神经网络识别工业字符需要达到较高的读取可靠性。为了实现这种高可靠性,引入了一种控制神经网络隐层结构的方法。该方法定义了隐藏层神经元的特征提取函数,并构造了初步教学,使隐藏层神经元具有定义的属性。在将期望的属性附加到隐藏层神经元之后,普通的反向传播过程改进了神经网络的结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structure of neural networks for industrial character reader
Neural networks to recognize industrial characters are required to achieve high reading reliability. To achieve this high reliability, a method to control the structure of a neural network's hidden layer has been introduced. The method defines the feature extraction functions of neurons in the hidden layer, and preliminary teaching is so constructed that it gives the hidden layer neurons defined properties. After the desired property attached to the hidden layer neurons, the ordinary backpropagation procedure refines the structure of the neural network.<>
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