Gu Tingwei, Kong Deren, Jiang Jian, Shang Fei, Chen Jing
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Rev Sci Instrum. 2016 Dec;87(12):125005. doi: 10.1063/1.4972826.
This paper applies back propagation neural network (BPNN) optimized by genetic algorithm (GA) for the prediction of pressure generated by a drop-weight device and the quasi-static calibration of piezoelectric high-pressure sensors for the measurement of propellant powder gas pressure. The method can effectively overcome the slow convergence and local minimum problems of BPNN. Based on test data of quasi-static comparison calibration method, a mathematical model between each parameter of drop-weight device and peak pressure and pulse width was established, through which the practical quasi-static calibration without continuously using expensive reference sensors could be realized. Compared with multiple linear regression method, the GA-BPNN model has higher prediction accuracy and stability. The percentages of prediction error of peak pressure and pulse width are less than 0.7% and 0.3%, respectively.
本文将遗传算法（GA）优化的反向传播神经网络（BPNN）应用于落锤装置产生压力的预测以及用于测量推进剂粉末气体压力的压电高压传感器的准静态校准。该方法能有效克服BPNN收敛速度慢和局部最小值问题。基于准静态比较校准方法的测试数据，建立了落锤装置各参数与峰值压力和脉冲宽度之间的数学模型，借此可实现无需持续使用昂贵参考传感器的实际准静态校准。与多元线性回归方法相比，GA-BPNN模型具有更高的预测精度和稳定性。峰值压力和脉冲宽度的预测误差百分比分别小于0.7%和0.3%。
