基于改进灰色模型预测的节流流量传感器测量校正方法
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摘要
为了提高节流流量传感器的测量校正精度,提出一种基于改进灰色GM(1,1)模型的预测校正方法。该方法首先对灰色GM(1,1)模型的流程进行改进,并采用LM方法对GM(1,1)模型的初始值进行求解计算;然后利用基于Markov过程的残差修正模型进一步提高预测精度;最后通过实例分析验证了提出方法的可行性。实验结果表明,相比传统灰色GM(1,1)模型,提出改进模型的预测值与实际测量值的拟合度更好,有效降低了校正后的节流流量传感器的测量误差。
A prediction correction method based on the improved gray model GM(1,1)is proposed to improve the measurement correction accuracy of the throttle flow sensor. The flow of the GM(1,1)model is improved by the proposed method,and the least square(LM)method is used to solve the initial value of the GM(1,1)model. The residual error modification model based on Markov process is adopted to further improve the prediction accuracy. The feasibility of the proposed method is verified with instance analysis. The experimental results show that,in comparison with the traditional GM(1,1) model,the improved model has higher fitting degree of predicted value and actual measured value,and can reduce the measurement error of the corrected throttle flow sensor effectively.
A prediction correction method based on the improved gray model GM(1,1)is proposed to improve the measurement correction accuracy of the throttle flow sensor. The flow of the GM(1,1)model is improved by the proposed method,and the least square(LM)method is used to solve the initial value of the GM(1,1)model. The residual error modification model based on Markov process is adopted to further improve the prediction accuracy. The feasibility of the proposed method is verified with instance analysis. The experimental results show that,in comparison with the traditional GM(1,1) model,the improved model has higher fitting degree of predicted value and actual measured value,and can reduce the measurement error of the corrected throttle flow sensor effectively.
引文
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[9] WANG Y W,SHEN Z Z,JIANG Y. Comparison of ARIMA and GM(1,1)models for prediction of hepatitis B in China[J].PLoS ONE,2018,63(7):80-85.
[2]郭素娜,张涛,孙立军,等.采用流场分析提高涡轮流量传感器性能的研究[J].仪器仪表学报,2015,36(11):2473-2478.GUO Suna,ZHANG Tao,SUN Lijun,et al. Research on improving the performance of turbine flow sensor by flow field analysis[J]. Chinese journal of scientific instrument,2015,36(11):2473-2478.
[3] REN W,CHO I H,ZHOU Z,et al. Ultrasensitive detection of microbial cells using magnetic focus enhanced lateral flow sensors[J]. Chemical communications,2016,52(27):4930-4933.
[4]徐宁,党耀国,丁松.基于误差最小化的GM(1,1)模型背景值优化方法[J].控制与决策,2015(2):283-288.XU Ning,DANG Yaoguo,DING Song. Background value optimization method for GM(1,1)model based on error minimization[J]. Control and decision,2015(2):283-288.
[5]王承庆.基于正弦变换和背景值优化的灰色GM(1,1)模型研究[J].重庆理工大学学报,2017(12):199-202.WANG Chengqing. Research on grey GM(1,1)model based on sinusoidal transformation and background value optimization[J]. Journal of Chongqing University of Technology,2017(12):199-202.
[6]邓罗泉,王立国,郑铁军,等.一种节流流量传感器测量滞后的校正方法[J].传感器与微系统,2015,34(3):11-14.DENG Luoquan,WANG Liguo,ZHENG Tiejun,et al. A method for correcting measurement hysteresis of throttle flow sensors[J]. Transducer and microsystem technologies,2015,34(3):11-14.
[7] YUAN C Q,CHEN D. Effectiveness of GM(1,1)model on linear growth sequence and its application in global primary energy consumption prediction[J]. Kybernetes,2016,45(9):1472-1485.
[8] LI M,WEI Y. A new method for solving the parameters in nonequidistant GM(1,1)power model[J]. Journal of grey system,2016,19(2):67-71.
[9] WANG Y W,SHEN Z Z,JIANG Y. Comparison of ARIMA and GM(1,1)models for prediction of hepatitis B in China[J].PLoS ONE,2018,63(7):80-85.