【摘要】 为解决软件可靠性预测精度差和泛化能力不强问题,提出一种遗传算法集成神经网络的软件可靠性预测模型。通过遗传算法对神经网络集成权重进行了优化,并用主成分分析方法对软件属性度量数据进行了预处理,降低数据维数,简化神经网络的结构,加快神经网络的运算速度。仿真实验结果表明,基于遗传算法集成神经网络的软件可靠性预测模型同BP网络、LVQ网络和PNN网络相比具有更好的预测精度和泛化能力。更多还原

【Abstract】 To solve the problem of software reliability prediction effectively,a model of neural network ensemble based on genetic algorithm(GA-NNE) is proposed,by the genetic algorithm neural network ensemble weights are optimized,and the method of principal component analysis is conducted on the experimental data pre-processing to reduce the dimension of the experimental data to simplify the structure of neural network,speed up the neural network computational speed.The simulation results show that the software reliability prediction model based on GA-NNE is effective in the prediction of software reliability,and has better precision than BP network,LVQ network and PNN network.更多还原