中文摘要：

为提高神经网络模型对软件可靠性预测结果的准确性和可信性,提出了一种基于多目标优化算法改进Elman网络模型Mop-IElman（multi-objective optimization-based improved Elman neural network）的方法：1）在Elman网络基础上,设计输出层的延迟反馈层,作为另一个状态层;2）以网络的结构和2个状态层的初始输出值为网络配置的变量,以网络的预测精度和顽健性为目标,采用NSGA-II（non-dominated sorting genetic algorithm II）进行多目标优化得到帕累托解,最大化网络预测精度与顽健性之和从而确定网络配置。通过两组实际软件失效数据对Mop-IElman进行实验验证,并与前馈网络、Elman网络、单目标优化Elman网络以及多目标优化Elman网络进行比较研究,结果表明Mop-IElman的预测结果具有较高的准确性和可信性。

英文摘要：

In order to improve accuracy and dependability of using neural network for software reliability prediction,a multi-objective optimization-based improved Elman recurrent network method（Mop-IElman） was proposed.First,on the basis of the Elman network,a self-delay feedback of the output layer as another context layer was designed.Second,the network architecture and the initial outputs of these two context layers were taken as variables of network configuration setting,and NSGA-II was employed to simultaneously optimize prediction performance and robustness,then the Pareto solution was obtained.After that,by maximizing the sum of prediction performance and robustness,the final network configuration setting was determined.Finally,the proposed method was compared with the feed-forward neural network,the Elman network,both the single-objective and the multi-objective optimization Elman networks with respect to two real software failure data.It demonstrated that the proposed Mop-IElman achieves higher prediction accuracy and de-pendability.