摘要
针对测量仪器校准间隔的优化问题,分析了历史校准数据的特征,建立了等维新息马尔可夫GM(1,1)预测模型.在等维新息GM(1,1)模型的基础上,引入马尔可夫模型,克服了随机波动数据对预测精度的影响.通过仿真实验对预测模型进行了验证,结果表明,等维灰色马尔可夫GM(1,1)模型的预测精度高于常规灰色GM(1,1)模型、等维新息灰色GM(1,1)模型和常规灰色马尔可夫GM(1,1)模型,更适合用于测量仪器校准间隔的预测.
To optimize the calibration interval of a measuring instrument, characters of historical calibration data are analyzed and an innovation Gray-Markov GM（ 1,1 ） model is established. The Markov chain method is presented based on the innovation GM（1,1） model, which reduces random fluctuation of calibration data. Experiments are also done to test the model. Results demonstrate that the precision of the innovation Gray-Markov GM（1,1） model is higher than the GM（1,1） model, the innovation GM（ 1,1 ） model and the Gray-Markov model. Therefore the model is a best model to forecast the calibration interval of a measuring instrument.
出处
《传感技术学报》
CAS
CSCD
北大核心
2007年第5期1095-1099,共5页
Chinese Journal of Sensors and Actuators
