Software cost estimation models using Radial Basis Function Neural Networks

被引：0
|
作者：
Idri, Ali [1 ]
Zahi, Azeddine [2 ]
Mendes, Emilia [3 ]
Zakrani, Abdelali [1 ]
机构：
[1] Mohamed 5 Univ, ENSIAS, Dept Software Engn, Rabat, Morocco
[2] Sidi Mohamed Ben Abdellah Univ, Dept Comp Sci FST, Fes, Morocco
[3] Univ Auckland, Dept Comp Sci, Auckland 92019, New Zealand
来源：
SOFTWARE PROCESS AND PRODUCT MEASUREMENT | 2008年 / 4895卷
关键词：
software effort estimation; neural networks; predictive accuracy; radial basis function neural networks;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Radial Basis Function Neural Networks (RBFN) have been recently studied due to their qualification as an universal function approximation. This paper investigates the use of RBF neural networks for software cost estimation, The focus of this study is on the design of these networks, especially their middle layer composed of receptive fields, using two clustering techniques: the C-means and the APC-III algorithms. A comparison between a RBFN using C-means and a RBFN using APC-III, in terms of estimates accuracy, is hence presented. This study uses the COCOMO'81 dataset and data on Web applications from the Tukutuku database.
引用
收藏
页码：21 / +
页数：3
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