Applied Mathematics and Mechanics (English Edition) ›› 2009, Vol. 30 ›› Issue (7): 853-864.doi: https://doi.org/10.1007/s10483-009-0705-x

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Classification using least squares support vector machine for reliability analysis

 GUO Zhi-Wei, BAI Guang-Chen   

  1. School of Jet Propulsion, Beijing University of Aeronautics and Astronautics, Beijing 100191, P. R. China
  • Received:2008-05-19 Revised:2009-05-18 Online:2009-07-01 Published:2009-07-01

Abstract: In order to improve the efficiency of the support vector machine (SVM) for classification to deal with a large amount of samples, the least squares support vector machine (LSSVM) for classification methods is introduced into the reliability analysis. To reduce the computational cost, the solution of the SVM is transformed from a quadratic programming to a group of linear equations. The numerical results indicate that the reliability method based on the LSSVM for classification has higher accuracy and requires less computational cost than the SVM method.

Key words: least squares, support vector machine, classification, reliability, performance function

2010 MSC Number: 

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