  Software development effort estimation is one of the most major activities in
software project management. A number of models have been proposed to construct
a relationship between software size and effort; however we still have problems
for effort estimation. This is because project data, available in the initial
stages of project is often incomplete, inconsistent, uncertain and unclear. The
need for accurate effort estimation in software industry is still a challenge.
Artificial Neural Network models are more suitable in such situations. The
present paper is concerned with developing software effort estimation models
based on artificial neural networks. The models are designed to improve the
performance of the network that suits to the COCOMO Model. Artificial Neural
Network models are created using Radial Basis and Generalized Regression. A
case study based on the COCOMO81 database compares the proposed neural network
models with the Intermediate COCOMO. The results were analyzed using five
different criterions MMRE, MARE, VARE, Mean BRE and Prediction. It is observed
that the Radial Basis Neural Network provided better results
