Computer Science ›› 2014, Vol. 41 ›› Issue (4): 172-177.

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Chaotic Neural Network Model for Software Reliability

ZHANG Ke,ZHANG De-ping and WANG Shuai   

  • Online:2018-11-14 Published:2018-11-14

Abstract: 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 fai-lure 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.

Key words: Empirical mode decomposition (EMD),Software reliability model,Software reliability prediction,Neural network mode,Chaos analysis

[1] Lyu M R.Handbook of software reliability engineering[M].New York:McGraw Hill,1996:56-60
[2] 赵亮,王建民,孙家广.统计测试的软件可靠性保障能力研究[J].软件学报,2008,19(6):1379-1385
[3] Yang B,Li X,Xie M,et al.A generic data-driven software reliability model with model mining technique[J].Reliability Engi-neering and System Safety,2010,95:671-678
[4] 邹丰忠,李传湘.软件可靠性混沌模型[J].计算机学报,2001,24(3):281-291
[5] Raja U,Hale D P,Hale J E.Modeling software evolution defects:a time series approach[J].Journal of Software Maintenance and Evolution-research and Practice,2009,21:49-71
[6] 贾治宇,康锐.软件可靠性预测的ARIMA方法研究[J].计算机工程和应用,2008,44(35):17-19
[7] Moura M C,Zio E,Lins I D,et al.Failure and reliability prediction by support vector machines regression of time series data[J].Reliability Engineering and System Safety,2011,96:1527-1534
[8] Lo J H.A study of applying ARIMA and SVM model to software reliability prediction[C]∥2011International Conference on Uncertainty Reasoning and Knowledge Engineering.2011:141-144
[9] 李海峰,陆民燕,王智新.基于灰色系统理论的软件可靠性综合评价框架[J].北京航空航天大学学报,2008,34(11):1261-1265
[10] 李海峰,陆民燕,曾敏,等.基因表达式编程在软件可靠性建模中的应用[J].计算机科学与探索,2011,5(6):534-546
[11] Su Y S,Huang C Y.Neural-network-based approaches for software reliability estimation using dynamic weighted combinationalmodels[J].The Journal of Systems and Software,2007,80:606-615
[12] Jin C,Jin S W,Ye J M,et al.Software reliability predictionbased on discrete wavelet transform and neural network[C]∥International Conference on Computational Intelligence and Software Engineering.2009:1-4
[13] Kiran N R,Ravi V.Software reliability prediction using wavelet neural networks[C]∥International Conference on ComputationalIntelligence and Multimedia Application.2007:195-199
[14] Hu Q P,Xie M,Ng S H.Software reliability predictions usingartificial neural networks[J].Computational Intelligence in Re-liability Engineering,2007,40:197-222
[15] Lo J H.The implementation of artificial neural networks applying to software reliability modeling[C]∥IEEE Control and Decision Conference,2009.CCDC’ 09.China,2009:4349-4354
[16] 玄兆燕,杨公训.经验模态分解法在大气时间序列预测中的应用[J].自动化学报,2008,34(1):97-101
[17] Han M,Xi J H,Xu S G,et al.Prediction of chaotic time series based on the recurrent network[J].IEEE Trans on Signal Processing,2004,52(12):3409-3416
[18] 张旭淘,贺国光,卢宇.一种在线实时快速地判定交通流混沌的组合算法[J].系统工程,2005,23(9):42-45
[19] 顾圣士,王志谦,程极泰.太阳黑子数时间序列的分形研究及预测[J].应用数学与力学,1999,20(1):81-86
[20] Weigend A S,Huberman B A,Rumelhart D E.Predicting the future:A connectionist approach[J].International Journal of Neural System,1990,1(3):1993-209
[21] 伍春香,刘琳,王葆元.三层BP 网隐层节点数确定方法的研究[J].计算机学报,1998(6):2-5
[22] 于青.关联维数计算的分析研究[J].计算机学报,2004(12):1-2
[23] 张德平,汪帅,周吴杰.基于EMD和GEP的软件可靠性预测模型[J].计算机科学,2013,40(4):164-168

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