计算机应用研究2016，Vol.33Issue(12)：3739-3742,3773,5.DOI:10.3969/j.issn.1001-3695.2016.12.049
基于D BN的软件可靠性预测模型的研究
Software reliability prediction model based on deep belief networks
王国涛 1石振国 2吴小景1
作者信息
- 1. 南通大学电子信息学院，江苏 南通226019
- 2. 南通大学计算机科学与技术学院，江苏 南通226019
- 折叠
摘要
Abstract
Safety-critical system is widely used in transportation,industrial control,aviation and other areas related to the na-tional security and people’s livelihood,it needs extremely high reliability.Control software is usually the core of security-criti-cal system,so its reliability prediction accuracy must be very high.This paper applied the deep belief networks (DBN)to the prediction accuracy of software reliability prediction model (SRPM).It used the dynamic mode-hopping MCMC (DMH)for the unsupervised learning of RBM which is the kernel module in DBN.The algorithm was based on dynamic maintenance a model set,with the help of the model-hopping to complete the state-hopping of RBM,making the unsupervised learning of the RBM have a high learning efficiency.The SGPM’s predictive ability established by DBN compared with SGPM’s predictive a-bility established by dynamic fuzzy neural network with parameters dynamic adjustment (SA-DFNN ) BP neural network (BPN)and the firefly algorithm of BP neural network (FABP).Simulation results confirm that the prediction of the SRPM based on DBN is highest and most stable.关键词
深度置信网络/软件可靠性预测模型/动态模式跳转/限制波尔兹曼机/无监督学习Key words
deep belief networks/software reliability prediction model/dynamic mode-hopping MCMC/restricted Boltzmann machine/unsupervised learning分类
信息技术与安全科学引用本文复制引用
王国涛,石振国,吴小景..基于D BN的软件可靠性预测模型的研究[J].计算机应用研究,2016,33(12):3739-3742,3773,5.
