Modeling and Predicting Tensile Strength of Tungsten Alloy by Using PSO-SVR

Abstract:

In this paper, the support vecstor regression (SVR) approach combined with particle swarm optimization (PSO) is proposed to establish a model for predicting tungsten tensile strength base on the tension experimental data of tungsten alloy under two influential factors, including tungsten content and deformation magnitude. Comparing the prediction result of PSO-SVR model with that of back propagation neural network (BPNN) model, it is shown that the prediction precision of SVR model is higher evaluated by identical training and test samples. The mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of SVR model, all are smaller than those of BPNN. This study suggests that SVR is an effective and powerful tool for predicting the tensile strength of tungsten alloy.

• Periodical: Advanced Materials Research (Volumes 455-456)
• Pages: 1497-1503
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.455-456.1497
• Online since: January 2012
• Authors: J.L. Tang, C.Z. Cai, X.J. Zhu, G.L. Wang, D.F. Cao
• Keywords: Modeling, Particle Swarm Optimization Algorithm (PSO), Predicting, Regression Analysis, Support Vector Regression (SVR), Tungsten Tensile Strength

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