摘要
介绍了温度在差压法气密性检测法中对检测精度的影响,然后提出了通过正向建模建立BP网络,并利用实验样本来训练网络。将训练好的神经网络用来预测高压气密性检测中需要检测的大体积容器内的温度,并将预测到的温度应用到智能气密性检测判断中,大大提高了高压气密性检测的精度。
The paper introduces the influence of temperature on the measurement precision of air-tightness differential pressure method. Then set up Back-Propagation Network and train it by using experiment data through Forward Modeling. The network is used in the temperature prediction in the contain which is needed to be detected, and in the leakage judgment. The research result can greatly improve the accuracy of high-pressure air -tightness.
出处
《自动化与仪器仪表》
2009年第5期48-50,共3页
Automation & Instrumentation
关键词
神经网络
温度预测
气密性检测
Neural network
Temperature prediction
Air-tightness detection
