基于RBF神经网络的软件成本估算模型

软件成本估算是软件开发过程中一项非常重要的活动，但现有的方法在准确估算软件成本方面还存在不足。针对软件成本估算不够准确的现状，提出了一种基于RBF神经网络的软件成本估算模型。该模型采用样本聚类的方法确定隐含层节点数，利用遗传算法对隐层节点中心值和高斯函数的宽度进行优化，利用线性最小二乘法训练网络的权值。实验证明，该模型能够准确有效地估算软件成本。

Software cost estimation model based on RBF neural network

Accurate software development cost estimation is very important for effective project management.So far,no model has been proved to be successful for predicting software development cost.As the accuracy of estimating software cost is not satisfactory,this study is designed to find a new more precise method.A novel radial basis function neural network is proposed to estimate the software cost.Hidden layer nodes are decided by sample cluster.Genetic Algorithm and the linear least squares algorithm are used as the learning method for this model.The experimental results show that model can estimate software cost accurately.