动态测量误差的贝叶斯建模预报

Bayes Modeling and Prediction for Dynamic Measurement Errors

针对动态测量过程的复杂性提出了动态测量误差序列的贝叶斯建模预报方法。该方法要求的数据量较少,且对序列无平稳性限制,并能充分利用预报过程中的主客观信息来修正模型,以提高预测精度。最后,通过仿真实例探讨了贝叶斯方法在动态测量误差建模预报中的适用性。

Bayes modeling and prediction method for dynamic measurement errors is presented.The method needn't a lot of data and the data used for modeling have no stationarity to limit.Furthermore,the subjective and objective information during prediction can be used to modify the model.The emulation result shows the suitability of Bayes principal in the modeling and prediction for dynamic measurement errors.