Combined Forecast of Calibration Interval Based on Linear Trend Model and Neural Network

Zhen Lin Chen; Fang Zhao; Xiao Zhang

doi:10.4028/www.scientific.net/AMM.635-637.662

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 635-637Combined Forecast of Calibration Interval Based on...

Combined Forecast of Calibration Interval Based on Linear Trend Model and Neural Network

Abstract:

For realizing the dynamic optimization of measuring instrument calibration interval, predicting the history calibration data by modeling. First the improved moving average method is used to modeling and to predict the development trend of parameters. On the basis of this, BP network is used to compensate the predicted residual sequence, so as to get more accurate forecasts. Then improved MA - BP prediction model is given to optimize the calibration interval dynamically. The model is verified through experiments. The results show that the model has higher prediction precision and better universality.

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Periodical:

Applied Mechanics and Materials (Volumes 635-637)

Pages:

662-665

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.635-637.662

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Cite this paper

Online since:

September 2014

Authors:

Zhen Lin Chen*, Fang Zhao, Xiao Zhang

Keywords:

BP Neural Network (BPNN), Calibration Interval, Combined Model, Moving Average

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* - Corresponding Author

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