The Contrastive Analysis of Predicting Riveting Pieces of Corrosion Fatigue Based on Multiple Kernel Function

Abstract:

In the process of long-term storage, the equipment would happen storage environment contaminated corrosion, mechanical structure stress corrosion damage. Currently,the corrosion fatigue damage prediction accuracy of method was low. Different kernel functions were adopted by this paper to compare based on least squares support vector machine (LSSVM). Besides, comparison was made among the BP neural network method, Standard Support Vector Machines (SVM), Grey System Prediction model Method and the radial basis function kernel least squares support vector machine (RBF_LSSVM) method by the simulation experiment. The optimal results finally were applied to practical engineering. The results showed that high accuracy and performance could be gained by employing the RBF_LSSVM method for predicting the trends of the mechanical structure rivet corrosion.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 587-592
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.587
• Online since: November 2014
• Authors: Jian Fei Chen*, Gang Jiang, Zi Sheng Li, Jian Feng Yang
• Keywords: Contrastive Analysis, LSSVM, Radial Basis Function Kernel (RBF), Riveting Pieces

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