西北工业大学学报
西北工業大學學報
서북공업대학학보
JOURNAL OF NORTHWESTERN POLYTECHNICAL UNIVERSITY
2014年
4期
637-641
,共5页
支持向量机(SVM)%遗传算法%神经网络%寿命预测%多应力
支持嚮量機(SVM)%遺傳算法%神經網絡%壽命預測%多應力
지지향량궤(SVM)%유전산법%신경망락%수명예측%다응력
support vector machines (SVM)%genetic algorithms%neural network%life prediction%multi-stress
针对电子元件在正常应力下的寿命预测,提出了基于遗传算法SVM的预测方法。首先进行多应力水平条件下的寿命实验,得到元件在各个应力下的失效时间,根据失效时间得出相应应力下的可靠性。然后将遗传算法与SVM相结合,建立预测模型,从而不仅可以预测同一应力下元件的寿命,可根据加速应力下元件的寿命来预测正常应力水平下的寿命。实验证明,在小样本条件下,该方法同神经神经网络相比,预测结果的精确度提高了14%,该预测方法能够更准确地预测出电子元器件的寿命。
針對電子元件在正常應力下的壽命預測,提齣瞭基于遺傳算法SVM的預測方法。首先進行多應力水平條件下的壽命實驗,得到元件在各箇應力下的失效時間,根據失效時間得齣相應應力下的可靠性。然後將遺傳算法與SVM相結閤,建立預測模型,從而不僅可以預測同一應力下元件的壽命,可根據加速應力下元件的壽命來預測正常應力水平下的壽命。實驗證明,在小樣本條件下,該方法同神經神經網絡相比,預測結果的精確度提高瞭14%,該預測方法能夠更準確地預測齣電子元器件的壽命。
침대전자원건재정상응력하적수명예측,제출료기우유전산법SVM적예측방법。수선진행다응력수평조건하적수명실험,득도원건재각개응력하적실효시간,근거실효시간득출상응응력하적가고성。연후장유전산법여SVM상결합,건립예측모형,종이불부가이예측동일응력하원건적수명,가근거가속응력하원건적수명래예측정상응력수평하적수명。실험증명,재소양본조건하,해방법동신경신경망락상비,예측결과적정학도제고료14%,해예측방법능구경준학지예측출전자원기건적수명。
To accurately predict the life of an electronic component under normal stress, a method based on genetic algorithm SVM is proposed. Firstly, after the test of accelerated life under several stress levels, the components time-out and corresponding reliability can be got. Then combining the genetic algorithm with SVM to build the pre-diction model, which can not only dope out the reliability under the same stress, but also can predict the compo-nents′life under normal stress level according to the life in accelerated life tests. Comparing with neural network this method can exactly predict the life of electronic components under the condition of small samples with improving the precondition accuracy by 14 percent, given in Figs. 1, 3 and 4 and Tables 2 and 3, and their com-parison.