Paper Title:
Gross Error Positioning and Processing Method Based on Forest Grey Modeling
  Abstract

The paper is committed to overcome the influence of gross error on the small quantity data of forest fire grey modeling. According to the quantity of the modeling data, Grey judgment of gross error and robust estimation theory is used separately for finding the gross error exit whether or not from the modeling data. And robust estimation theory and LIR algorithm can be used to process the gross error. From the examples, A quarter of fitting precision of robust estimation is less than 1%, and 75% is 1~5%; and half of fitting precision of LIR algorithm is less than 1%, and half is 1~5%. That is to say LIR algorithm provides a rapid, simple and practical way to build model of data which contains gross error or which contain missing data.

  Info
Periodical
Chapter
Chapter 9: Information System for Materials and Mechanics
Edited by
Linli Xu, Wenya Tian and Elwin Mao
Pages
1262-1267
DOI
10.4028/www.scientific.net/AMM.80-81.1262
Citation
Y. Li, Z. W. Wang, S. N. Tang, "Gross Error Positioning and Processing Method Based on Forest Grey Modeling", Applied Mechanics and Materials, Vols. 80-81, pp. 1262-1267, 2011
Online since
July 2011
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Price
$32.00
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