Gross Error Positioning and Processing Method Based on Forest Grey Modeling

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 similar to 5%; and half of fitting precision of LIR algorithm is less than 1%, and half is 1 similar to 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.