基于学习向量量化神经网络的软件可靠性预测

针对传统的软件可靠性预测模型在实际应用中存在预测泛化性能不佳等问题，提出一种基于学习向量量化（LVQ）神经网络的软件可靠性预测模型。首先分析了LVQ神经网络的结构特点以及它与软件可靠性预测的联系，然后运用该网络来进行软件可靠性的预测，并基于美国国家航空航天局（NASA）软件数据项目中的实例数据集，运用Matlab工具进行了仿真实验。通过与传统预测方法的对比，证明该方法具有可行性和较高的预测泛化性能。

Software reliability prediction based on learning vector quantization neutral network

The application of traditional software prediction model has poor generalized performance.This paper put forward a software reliability prediction model based on Learning Vector Quantization(LVQ) neural network.First,this paper analyzed the structure characteristics of LVQ neural network and its relation with software reliability prediction.Then the network was used to predict the software reliability.In the end,the authors confirmed the algorithm through multiple simulation experiments under the Matlab environment and the data from Metrics Data Program(MDP) database of National Aeronautics and Space Administration(NASA) of USA.The experimental results indicate that the method is feasible and has a higher prediction precision than the traditional software prediction method.