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DOI: 10.14569/IJACSA.2011.021122
PDF

Adaptive Outlier-tolerant Exponential Smoothing Prediction Algorithms with Applications to Predict the Temperature in Spacecraft

Author 1: Hu Shaolin
Author 2: Li Ye
Author 3: Zhang Wei
Author 4: Fan Shunxi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 11, 2011.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The exponential smoothing prediction algorithm is widely used in spaceflight control and in process monitoring as well as in economical prediction. There are two key conundrums which are open: one is about the selective rule of the parameter in the exponential smoothing prediction, and the other is how to improve the bad influence of outliers on prediction. In this paper a new practical outlier-tolerant algorithm is built to select adaptively proper parameter, and the exponential smoothing prediction algorithm is modified to prevent any bad influence from outliers in sampling data. These two new algorithms are valid and effective to overcome the two open conundrums stated above. Simulation and practical results of sampling data from temperature sensors in a spacecraft show that this new adaptive outlier-tolerant exponential smoothing prediction algorithm has the power to eliminate bad infection of outliers on prediction of process state in future.

Keywords: Exponential prediction; Adaptive smoothing prediction; Outlier-tolerance smoothing prediction.

Hu Shaolin, Li Ye, Zhang Wei and Fan Shunxi, “Adaptive Outlier-tolerant Exponential Smoothing Prediction Algorithms with Applications to Predict the Temperature in Spacecraft” International Journal of Advanced Computer Science and Applications(IJACSA), 2(11), 2011. http://dx.doi.org/10.14569/IJACSA.2011.021122

@article{Shaolin2011,
title = {Adaptive Outlier-tolerant Exponential Smoothing Prediction Algorithms with Applications to Predict the Temperature in Spacecraft},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.021122},
url = {http://dx.doi.org/10.14569/IJACSA.2011.021122},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {11},
author = {Hu Shaolin and Li Ye and Zhang Wei and Fan Shunxi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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