A novel whale-based algorithm for optimizing the ANN approach: application to temperature compensation in pressure scanner calibration systems
Huan Wang, Qinghua Zeng, Zongyu Zhang, Yijun Zou
Abstract
Abstract To resolve the nonlinear measured values of pressure scanners with temperature change during pressure measurements, resulting in low accuracy, this paper proposes and develops a new calibration system based on neural networks for pressure scanners. Specifically, we first calibrate the pressure scanner production process, then design the sealing device applicable to the working environment of pressure scanners, and finally install the calibration system of the pressure scanner for calibration experiments to obtain accurate measurement results. In addition, at the algorithmic level, this paper forwards a whale optimization algorithm-based backpropagation neural network method instead of the traditional least squares method to complete the temperature compensation. Both the offline compensation results and the compensated online experimental results exhibit the high measurement accuracy of this temperature compensation method. The full-scale (FS) error is 0.07%, the coefficient of determination ( R 2 ) = 99.27%, with similar results for all channels of the pressure sensor in the temperature environment of −40 °C to 60 °C and absolute pressures ranging from 0 to 1.1 MPa. This result presents a significant reduction in the FS error of 0.25%, R 2 = 91.32% based on the conventional algorithm compensation, and also applies to pressure scanners with wide temperature region, wide range, and high accuracy temperature compensation, which is crucial for their future research of low-cost overall calibration and high accuracy algorithms.