| 利用BP神经网络确定巷道摩擦阻力系数 |
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| 引用本文： | 张攀,周志强,刘剑.利用BP神经网络确定巷道摩擦阻力系数[J].辽宁工程技术大学学报(自然科学版),2003,22(4):536-537. |
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| 作者姓名： | 张攀 周志强 刘剑 |
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| 作者单位： | 辽宁工程技术大学资源与环境工程学院,辽宁,阜新,123000 |
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| 摘 要： | 在特定圆木支护巷道条件下，合理地选择了木支架直径、支架纵口径，巷道面积、巷道周长、支护周边长等作为神经网络的输入参数，运用改进的BP人工神经网络模型。进行摩擦阻力系数的模式识别。该方法的主要优点有：网络结构简单，收敛速度快。预测精度高。模型精度很高，有一定的实用价值。
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| 关 键 词： | 巷道支护 摩擦阻力系数 模式识别 BP神经网络 圆木支护 巷道面积 巷道周长 |
| 文章编号： | 1008-0562(2003)04-0536-02 |
| 修稿时间： | 2003年6月15日 |
Applying BP neural network model to calculate coefficient of frictional resistance of roadways |
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| ZHANG Pan,ZHOU Zhi-qiang,LIU Jian.Applying BP neural network model to calculate coefficient of frictional resistance of roadways[J].Journal of Liaoning Technical University (Natural Science Edition),2003,22(4):536-537. |
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| Authors: | ZHANG Pan ZHOU Zhi-qiang LIU Jian |
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| Abstract: | Under the special conditions of roadways of timbering bracket, this model can rationally select the diameter of log bracket, lengthways proportion of bracket, area of roadway and perimeter of roadway, and others as input value and apply an improved BP artificial neural network on pattern recognition of coefficient of frictional resistance. The main advantages of it include simple structure, efficient convergence, precise forecasting, etc. The precision in calculation of model is higher, it has a certain practical value. |
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| Keywords: | coefficient of frictional resistance pattern recognition neural network |
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