Defect Recognition of X-Ray Steel Rope Cord Conveyer Belt Image Based on BP Neural Network

Wang Wen, Miao Chang-yun, Wang Ji, Li Xian-guo
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引用次数: 1

Abstract

BP neural network is used to recognize X-ray steel rope cord conveyer belt image with defect in this paper. Firstly, the model of three layers BP neural network is established, and it is made up of 240 input nodes, 20 hidden layer nodes, and 1 output node. Then, the BP neural network is trained and tested in MATLAB. The results show that X-ray steel rope cord conveyer belt image with defect can be identified by the neural network.
基于BP神经网络的x射线钢丝绳输送带图像缺陷识别
本文采用BP神经网络对x射线带缺陷钢丝绳输送带图像进行识别。首先,建立三层BP神经网络模型,该网络由240个输入节点、20个隐藏层节点和1个输出节点组成。然后在MATLAB中对BP神经网络进行训练和测试。结果表明,该神经网络可以对带缺陷的x射线钢丝绳输送带图像进行识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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