CAJ | 학술논문

　　基于经验模态分解结合支持向量回归算法与灰色系统理论提出一种混合软件可靠性预测模型，通过对原始软件失效数据使用经验模态分解方法进行预处理，将失效数据分解得到不同频段的本征模态分量和剩余分量，用支持向量回归算法对本征模态分量进行预测，用灰色系统模型GM（1，1）对剩余分量进行预测，然后将预测结果进行重构，得到最终软件可靠性预测值。为了验证所提混合预测模型的有效性，利用两组真实软件失效数据，与SVR可靠性预测模型和GM（1，1）可靠性预测模型进行实验对比分析，实验结果表明，所提混合预测模型较这两种可靠性预测模型具有更精确的预测精度。
　　기우경험모태분해결합지지향량회귀산법여회색계통이론제출일충혼합연건가고성예측모형，통과대원시연건실효수거사용경험모태분해방법진행예처리，장실효수거분해득도불동빈단적본정모태분량화잉여분량，용지지향량회귀산법대본정모태분량진행예측，용회색계통모형GM（1，1）대잉여분량진행예측，연후장예측결과진행중구，득도최종연건가고성예측치。위료험증소제혼합예측모형적유효성，이용량조진실연건실효수거，여SVR가고성예측모형화GM（1，1）가고성예측모형진행실험대비분석，실험결과표명，소제혼합예측모형교저량충가고성예측모형구유경정학적예측정도。
A hybrid software reliability prediction model based on Empirical Mode Decomposition(EMD)is presented and applied to software reliability forecasting. The software failure samples are handled in order to obtain the Intrinsic Mode Func-tions(IMFs)and the residue of different frequency bands are obtained according to EMD. Then the corresponding failure data series in the IMFs and the residue are chosen as the training samples. By means of the flexible prediction capacity of Support Vector Regression(SVR)and Grey Model(GM), the models of each IMF and the residue are forecasted. The ultimate forecasting result is obtained by reconstructing the forecasting results of each IMF and the residue. The method of EMD overcomes the shortcomings that it’s difficult to select proper wavelet function for wavelet transform, and the final result indicates that the IMFs can reflect the characteristic of software failure. After comparing with the results forecasted by means of combination of SVR and GM(1, 1), it proves that the effect of the hybrid forecasting method of SVR&GM in software reliability forecasting is better.