Input data analysis using neural networks

Simulation deals with real-life phenomena by constructing representative mo dels of a system being questioned. Input data provide a driving force sor s uch models. The requirement for identifying the underlying distributions of data sets is encountered in many fields and simulation applications (e.g., manufacturing economics, etc.). Most of the time, after collection of the raw data, the true statistical distribution is sought by the aid of nonpara metric statistical methods. In this paper, we investigate the feasibility o f using neural networks in selecting appropriate probability distributions. The performance of the proposed approach is measured with a number of test problems.