Pressure Sensor Error Compensation Algorithm Based on MEA-BP Neural Network

• Published in: 水下无人系统学报 Vol. 31; no. 2; pp. 252 - 258
• Main Authors: Hao SHI, Hui FAN, Jianchen LI, Runhui ZHAO, Ya LI
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.04.2023
• Subjects: back propagation neural network; error compensation; mind evolutionary algorithm; pressure sensor
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.202205002

Piezoresistive pressure sensors are sensitive to environmental changes. Ambient temperature changes would produce thermal drift, which affects sensor performance. This study entailed the use of the mind evolutionary algorithm(MEA)-back propagation(BP) neural network algorithm to establish an error compensation model for piezoresistive pressure sensors. The model uses the MEA algorithm to optimize the initial weight and threshold of the neural network, which reduces the possibility of the training falling into local optimization owing to the uncertainty of the initial value. The Levenberg–Marquardt algorithm replaces the gradient descent method to accelerate the convergence speed of the neural network and increase the reliability of the compensation algorithm. The results of the simulation show that, compared with those of the BP neural network compensation algorithm and genetic algorithm(GA)-BP neural network, the expectation of the root mean square error of the MEA-BP algorithm is lower by 48.7% and 8.29%, r