A novel pressure sensor calibration system based on a neural network

【摘要】 According to the specific input–output characteristics of a pressure sensor, a novel calibration algorithm is presented and a calibration system is developed to correct the nonlinear error caused by temperature. In contrast to the routine BP and RBF, curve fitting based on RBF is first used to get the slope and intercept, and then the voltage–pressure curve is described. Test results show that the algorithm features fast convergence speed, strong robustness and minimum SSE(sum of squares for error). It is proven by practical applications that this calibration system works well and the measurement precision is better than the design demands. Furthermore, this calibration system has a good real-time capability.更多还原

【Abstract】 According to the specific input–output characteristics of a pressure sensor, a novel calibration algorithm is presented and a calibration system is developed to correct the nonlinear error caused by temperature. In contrast to the routine BP and RBF, curve fitting based on RBF is first used to get the slope and intercept, and then the voltage–pressure curve is described. Test results show that the algorithm features fast convergence speed, strong robustness and minimum SSE(sum of squares for error). It is proven by practical applications that this calibration system works well and the measurement precision is better than the design demands. Furthermore, this calibration system has a good real-time capability.更多还原