软件可靠性混沌神经网络模型  

Chaotic Neural Network Model for Software Reliability

作　　者：张柯[1] 张德平[1] 汪帅[1] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016

出　　处：《计算机科学》2014年第4期172-177,共6页Computer Science

基　　金：中央高校基本科研业务费专项资金(NS2012072)资助

摘　　要：基于经验模态分解算法、混沌分析和神经网络理论提出了一种软件可靠性建模及预测的混沌神经网络模型。首先应用经验模态分解算法把软件失效数据序列分解成不同尺度的基本模态分量,并在此基础上进一步分析,表明软件失效数据是否存在混沌特性;再经神经网络进行组合预测,提高模型对目标函数的学习能力,有效提高预测精度;最后基于两组真实软件失效数据集,将所提出的方法与基于支持向量回归机以及单纯使用神经网络的软件可靠性预测模型进行比较分析。结果表明,基于混沌分析、结合经验模态分解和神经网络的软件可靠性预测模型具有更为显著的模型拟合能力与精确的预测效果。A forecasting method based on Empirical Mode Decomposition （EMD）,chaos analysis and neural network theory was presented to model and forecast software reliability forecasting.First,using EMD theory,the software failure data time serial is decomposed into many intrinsic modal functions （IMF） which are an significantly represent potential information of original time serial,and the further analysis of IMF indicates whether software failure has a chaos feature.Then,by using chaos theory and neural network,the forecasting models are established to forecast the IMF respectively.By these means,the model can be improved to learn various objective functions and more precious prediction can be obtained.After comparing the results forecasted by means of combination of SVR and neural network,it is proved that the effect of the forecasting method of EMD＆GEP in software reliability forecasting is better.

关 键 词：经验模态分解 软件可靠性模型 软件可靠性预测 神经网络模型 混沌分析 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]