Application of back propagation neural network technology on diamond test
-
摘要: 以金刚石外部形貌参数椭圆度、透光度和其磁化率为输入量,以金刚石的冲击韧性TI值、热冲击韧性TTI值为输出量, 通过BP神经网络建立输入与输出量之间的映射关系,得到金刚石TI、TTI的BP神经网络预测值。结果表明:TI、TTI的预测结果较为准确,其预测值与实际检测值平均相对误差不高于1.4%,最大相对误差不高于5.4%,在一定程度上可以替代当前的有损检测方法。Abstract: The ellipticity, transmittance and magnetic susceptibility of diamond are taken as input and the impact toughness (TI) and thermal impact toughness (TTI) as output to establish the mapping relationship between the inputs and outputs by BP neural network. Then a BP neural network prediction on TI and TTI values of diamonds are obtained. The results show that the prediction of TI and TTI values are relative accurate. The average relative error rate between the predicted values and the detected ones is not higher than 1.4%, and the maximum relative error rate is not higher than 5.4%. Therefore, the BP neural network prediction could replace the current damage detection methods to some extent.
点击查看大图
计量
- 文章访问数: 123
- HTML全文浏览量: 18
- PDF下载量: 10
- 被引次数: 0