For the problem in reducing cross-sensitivity of multi-sensors by linear regression
a method of normal equations based on the theory of least squares and matrix computations is proposed to solve the coefficient of fitting curved surface equation in the inverse model of multi-sensors
which is simple in programming to evaluate the coefficient of multivariable and higher order equation. The experiment shows that it can obviously reduce the multi-sensor cross-sensitivity
and improve nearly two orders of magnitude for the temperature coefficient of pressure sensor sensitivity
more than one order of magnitude or the electricity coefficient of sensor sensitivity
and three times for the temperature coefficient of zero position
especially for higher orders of fitting curves surface equations; algorithm.
LI Qiang, LIANG Li, LIU Zhen, et al. Intelligent pressure sensor system with temperature compensation[J]. Chinese Journal of Scientific Instrument, 2008, 29(9):1934-1937.
ZHANG Zhulin, YANG Zhenkun, WU Huihua, et al. Temperature and strain-sensed researches of double fiber Bragg gratings[J]. Journal of Xi'an Jiaotong University, 2004, 38(6): 607-610.
LI Guoli, LI Zhiquan. Research of the temperature compensation for strain sensing measurement of fiber Bragg grating[J]. Laser Optoelectronics Progress, 2005, 42(4): 25-28.
GAO Feng, DONG Haiying, HU Yankui, et al. Sensor cross-sensitivity restraining based on BP neural network[J]. Journal of Transducer Technology, 2005, 24(2): 22-26.
BI Haijun,FU Shengxue,FENG Zhuncheng, et al. RBF neural network temperature compensation for a pressure transducer based on Labview[J]. Periodical of Ocean University of China, 2004, 34(6): 1041-1044.