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Study of the neural network application in handwritten-digit recognition,

机译:神经网络在手写数字识别中的应用研究,

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Abstract: In this paper, Hopfield networks, Hamming networks, and neocognitron models and their application in handwritten digit recognition are discussed. The neocognitron model is a multilayer network for a mechanism of visual pattern recognition and self-organized by `learning without a teacher,' and it acquires an ability to recognize stimulus patterns based on the geometrical similarity of their shapes without being affected by their positions and distortions, so it showed higher ability to recognize handwritten digits. We developed a handwritten digit recognition system based on the neocognitron (HDRSBN), and carried on the simulation experiments. !6
机译:摘要:本文讨论了Hopfield网络,Hamming网络和neocognitron模型及其在手写数字识别中的应用。新认知模型是用于视觉模式识别和通过“无师自学”的自组织机制的多层网络,它具有基于形状的几何相似性识别刺激模式的能力,而不受位置和位置的影响。变形,因此它显示出更高的识别手写数字的能力。我们开发了基于新认知器(HDRSBN)的手写数字识别系统,并进行了仿真实验。 !6

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