Pressure prediction model based on artificial neural network optimized by genetic algorithm and its application in quasi-static calibration of piezoelectric high-pressure sensor

Tingwei Gu; Deren Kong; Jian Jiang; Fei Shang; Jing Chen

doi:10.1063/1.4972826

Pressure prediction model based on artificial neural network optimized by genetic algorithm and its application in quasi-static calibration of piezoelectric high-pressure sensor

Rev Sci Instrum. 2016 Dec;87(12):125005. doi: 10.1063/1.4972826.

Authors

Tingwei Gu¹, Deren Kong¹, Jian Jiang¹, Fei Shang¹, Jing Chen¹

Affiliation

¹ School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

PMID: 28040905
DOI: 10.1063/1.4972826

Abstract

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.