Research on combination forecast method of instrument precision

Precision is important in judging measure instruments quality and tracing to the source of measure errors. recision forecast present an effective precision control methods, but forecast and combined forecast technology is researched less in measuring instruments precision forecast. The theory of Linear combination forecast is very simple and it be applied in many projects, but it has some drawbacks, for resolve which, nonlinear combination forecast is designed. Built respectively the ARMA (Auto-Regression and Moving-Average), BP-NN, FNN (Fuzzy Neural Networks) and GM (Grey Model) using the history time series data. Then by using their forecast results design BP-NN combination forecast model to output the final forecast result. MSE (Mean Square Error) of every model forecast outputs is regarded as checking criterion to compare their forecast precision. The experiment results showed that BP-NN combined forecasting method had better forecasting precision compared with single ones and its forecast precision is better than optimal linear combination forecast method´s.