Abstract
Corrosion prediction is a technology of finding the corrosion law based on material corrosion data. Due to corrosion data has the characteristics of high dimensional nonlinearity, randomness and limited sizes, many data modeling methods based on large samples are not applicable. In the process of corrosion prediction, we have to deal with missing data values, outlier detection, feature selection and regression. However, feature selection and regression would be the focus of our research in this paper. This paper adopts a modeling method combining of Grey Relational Analysis and Support Vector Regression, referred to as GRA-SVR, the former is used to select feature and the latter is used for regression. The experimental results show that, GRA-SVR method achieves higher precision than other methods such as BP Neural Network.
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Fu, D., Xiang, J., Li, X. (2013). Research and Application of Corrosion Prediction Based on GRA-SVR. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_33
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DOI: https://doi.org/10.1007/978-3-642-42057-3_33
Publisher Name: Springer, Berlin, Heidelberg
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