IScIDE 2013: Intelligence Science and Big Data Engineering pp 255-262 | Cite as
Research and Application of Corrosion Prediction Based on GRA-SVR
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.
Keywords
Corrosion Prediction Grey Relational Analysis Support Vector RegressionPreview
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