The parameters of dynamic fuzzy neural network were dynamically adjusted by genetic algorithm (GA-DFNN),and GA-DFNN was used to study software reliability growth model(SGRM). The optimal solution of DFNN' s parameters was resolved by genetic algorithm in the DFNN's training process,and according to the DFNN which has the optimal parameters, software failure data prediction model was established. According to 3 groups of software defects data, we compared the SGRM's predictive ability established by GA-DFNN with SGRM's predictive ability established by fuzzy neural network(FNN) and BP neural network(BPN). The simulation results confirm that the SRGM established by GA-DFNN has steady short period prediction, and its short period prediction error is small and it has some versatility.%利用遗传算法对动态模糊神经网络的自身参数进行动态调整(GA-DFNN),并将其应用于软件可靠性增长模型(SRGM)的研究.在对动态模糊神经网络进行训练的过程中,用遗传算法求得动态模糊神经网络自身参数的优化解,根据得到的参数建立基于动态模糊神经网络的软件失效数据预测模型.利用3组软件缺陷数据,对用GA-DFNN建立的SRGM和模糊神经网络(FNN)以及BP神经网络(BPN)建立的SRGM的预测能力进行了比较,仿真结果证实,根据GA-DFNN建立的SRGM的短期预测能力稳定,短期预测误差小,且具有一定的通用性.
展开▼
