应用BP神经网络进行了管道缺陷参数的定量识别，识别结果的误差小于10%，完全满足实际检测的要求。
Use BP neural network to quantitatively recognize pipeline defect parameter, the error of recognition result is below 10%, and requirement of practical inspection can be completely fulfilled.
分别采用人工神经网络法和逐步回归分析法对原油管道蜡沉积实验数据进行分析处理，建立蜡沉积速率模型。
As the artificial neural network and stepwise regression analysis are respectively applied to the data of crude wax deposition experiment, the model of wax deposition velocity was established.
为了解决目前管道流量泄漏监测和定位问题，根据管道泄漏应力波和正常信号的区别，建立神经网络模型。
To solve the leak detection and location problem in pipeline flux, we built neural network model based on the difference of leak wave and normal signal.
在试验研究结果和前人工作基础上，利用人工神经网络法对管道内浆体摩阻损失进行拟合和预测。
Based on experimental results and previous works, frictional loss of slurry in pipe has been simulated and predicated by artificial neural network.
在前人试验研究工作的基础上，利用人工神经网络法对大直径浆体输送管道的淤积临界流速进行了拟合和预测。
Based on the previous experimental works, critical deposition velocity in large pipe has been fitted and predicated by artificial neural network.
为了解决目前管道流量泄漏监测和定位问题，根据管道泄漏应力波和正常信号的区别，建立神经网络模型。
On the underground booster and leakage of the pipeline flux was developed a remote leakage monitoring and locating system based on GSM.
为了解决目前管道流量泄漏监测和定位问题，根据管道泄漏应力波和正常信号的区别，建立神经网络模型。
On the underground booster and leakage of the pipeline flux was developed a remote leakage monitoring and locating system based on GSM.
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