An Estimation Model of Research Cost Based on Rough Set and Artificial Neural Network

Abstract

The problem of research cost estimation is a typical multi-factors estimation issue, which has not been solved satisfactorily. A method integrating rough sets theory and artificial neural network is presented to estimate cost. In term of the important degree of input influencing factor to output, rough set approach and the conception of information entropy are employed to reduce the parameters of the input parameter set with no changing classification quality of samples. Thus, the number of the input variables and neurons is gotten, and the cost estimation model based on rough set and BP artificial network is set by learning from the original data of typical samples. At last, its application to the cost estimation of missile system is given. It was shown that the approach can reduce the training time, improve the learning efficiency, enhance the predication accuracy, and be feasible and effective.

The research was supported by the Doctorate Foundation of the Engineering College, Air Force Engineering University(BC0504).