基于连续型深度置信神经网络的软件可靠性预测

为了提高软件可靠性智能预测的精度,采用连续型深度置信神经网络算法用于软件可靠性预测.首先提取影响软件可靠性的核心要素样本,并获取样本要素的关键特征;然后建立连续型深度置信神经网络(Deep Belief Network,DBN)的软件可靠性预测模型,输入待预测样本,通过多个受限波尔兹曼机(Restricted Bolt...

Software Reliability Prediction Based on Continuous Deep Confidence Neural Network

In order to improve the accuracy of intelligent prediction of software reliability,continuous depth confidence neural network algorithm is used for software reliability prediction.Firstly,the core elements samples that affect software reliability are extracted,and the key features of the sample elements are obtained.Then,a software reliability prediction model based on conti-nuous deep belief neural network(DBN)is established.The samples to be predicted are input,and the parameters such as DBN weight are obtained through pre-processing training of multiple Restricted Boltzmann Machine(RBM)layers and multiple reverse fine-tuning iterations until the maximum number of RBM layers and the maximum number of reverse fine-tuning iterations are reached.Finally,a stable software reliability prediction model is obtained.Experiments show that good software reliability prediction accuracy and standard deviation can be obtained by reasonably setting the number of nodes in the hidden layer of DBN and the learning rate.Compared with commonly used software reliability prediction algorithms,this algorithm has high prediction accuracy,small standard deviation and high applicability in software reliability prediction.