Software Reliability Prediction Model Based on Neural Networks and Particle Swarm Optimization
Huafeng Yu · 2011
Study on software reliability prediction model.RBF neural network is one of the most important software reliability prediction methods,but the search speed of traditional RBF neural network parameters optimization method is low and blindfold,and the software reliability prediction error rate is high,therefore,it is difficult to obtain optimal parameters.In order to improve the prediction accuracy of software reliability,a software reliability prediction model is proposed based on neural network and particle swarm optimization in this paper.Firstly,RBF neural network of initial parameters are used as the particle,and the software reliability and accuracy are used as the objective function of the particle of he particle swarm optimization.Then through collaboration of the particle swarms,the optimal parameters of the RBF neural network are obtained,and finally with optimal parameters of RBF neural network,software reliability is predicted.Using an application database to test and analysis the model,the results show that compared with the traditional RBF neural network,RBF neural network model based on particle swarm optimization has improved the prediction accuracy of the software reliability,the convergence speed is fast,and it is very suitable to software reliability prediction.