Software reliability prediction with an improved Elman network model
Software reliability prediction with an improved Elman network model
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作者：
Guo Ping
为了提高神经网络用于软件可靠性预测的准确性和可靠性，提出了一种基于多目标优化的改进Elman递归网络方法首先，在Elman网络的基础上，设计了一个输出层作为另一个上下文层的自延迟反馈，然后，将输出层作为上下文层的自延迟反馈输出，并将输出层作为上下文层的自延迟反馈输出。将网络结构和这两个上下文层的初始输出作为网络配置设置的变量，利用NSGA-II对预测性能和鲁棒性进行同时优化，得到Pareto解，然后通过最大化预测性能和鲁棒性之和，确定最终的网络配置设置，最后将该方法与前馈神经网络进行比较，对两个真实的软件失效数据进行了单目标和多目标优化Elman网络的预测，结果表明，所提出的Mop-IElman网络具有较高的预测精度和可靠性。
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.