基于灰色Elman神经网络软件可靠性预测模型

Software reliability prediction model based on grey Elman neural network

针对当前软件可靠性预测模型在随机性和动态性较强的可靠性现场数据中存在预测精度波动比较大、适应性比较差的问题,提出一种基于灰色Elman神经网络的软件可靠性预测模型.首先使用灰色GM(1,1)模型对失效数据进行预测,弱化其随机性;然后采用Elman神经网络对GM(1,1)的预测残差进行建模预测,捕捉其动态性变化规律;最后将GM(1,1)预测值和Elman神经网络残差预测值相结合得到最终的预测结果.使用航班查询系统的现场失效数据集进行了模型仿真实验,并将灰色Elman神经网络预测模型与反向传播(BP)神经网络、Elman神经网络预测模型进行比较,其对应的均方误差(MSE)和平均相对误差(MRE)分别为105.1、270.9、207.5和0.001 1、0.002 1、0.001 6,并且灰色Elman神经网络预测模型的误差均为最小值.实验结果表明该模型具有较好的预测精度.

The current software reliability prediction model has big prediction accuracy fluctuation and poor adaptability in field data of reliability with strong randomness and dynamics. In order to solve the problems, a software reliability prediction model based on grey Elman neural network was proposed. First, the grey GM （ 1, 1） model was used to predict the failure data and weaken its randomness. Then the Elman neural network was utilized to build the model for predicting the residual produced by GM （ 1, 1）, and catch the dynamic change rules. Finally, the prediction results of GM （ 1, 1） and Elman neural network residual were combined to get the final prediction outcomes. The simulation experiment was conducted by using field failure data set produced by the flight inquiry system. The gray Elman neural network model was compared with Back- Propagation （BP） neural network model and Elman neural network model, the corresponding Mean Squared Error （MSE） and Mean Relative Error （MRE） of the three models were respectively 105.1, 270.9, 207.5 and 0. 0011, 0. 0021, 0. 0016. The errors of gray Elman neural network prediction model were the minimum. The experimental results show that the proposed gray Elman neural network prediction model has higher prediction accuracy.