Specification and prediction of net income using by generalized regression Neural Network (A case study)

Abstract

Forecasting the future of mining activity is noted to be the most important purpose of decision makers. Net income is a particular parameter that plays significant role in gaining the attention of investors. It is demonstrated that by indicating key parameters affecting on the net income, prediction of net income will be considerably successful. Thus, the aim of this paper is to use an artificial intelligence method named generalized regression neural network (GRNN) for prediction of net income by taking into consideration of discounted cash flow table and six important parameters namely number of competitor, sales volume, annual cost, supply and demand, tax rate and inflation rate. Considering the six expressed parameters and Jade mine, Iran as case study, GRNN has shown appropriate result in the both training and testing step. As a result, GRNN has introduced itself as a robust method in the wide variety application of regression tasks.