@inbook{df6ec21cb0ea4c6e9825ae6691bc432e,
title = "Life and reliability prediction of the multi-stress accelerated life testing based on grey support vector machines",
abstract = "There are many difficulties in statistical analysis of multi-stress accelerated life testing, such as establishing the accelerated model and solving pluralism likelihood equations. With a focus on these difficulties, the Grey-SVM based life and reliability prediction method for multi-stress accelerated life testing is proposed, with the accelerated stress level and the reliability as SVM inputs, and the corresponding Grey AGO processing failure data as outputs. Simulation and case study shows that the method has high prediction accuracy and with less amount of training samples than neural network.",
author = "F. Sun and X. Li and T. Jiang",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag London 2014.",
year = "2014",
doi = "10.1007/978-1-4471-4993-4\_25",
language = "英语",
series = "Lecture Notes in Mechanical Engineering",
publisher = "Springer Heidelberg",
pages = "273--282",
booktitle = "Lecture Notes in Mechanical Engineering",
address = "德国",
}