Abstract:
This research outlines a novel approach to obtaining mathematical models from neural networks. The target scenario
is one where a response variable depends on a number of factors, each factor has an effect which is a function of the
factor and the response variable is the sum of the effects of the factors. A neural network was trained such that response
values were generated from factor values. It was assumed that each effect was zero when the underlying factor was set
to zero. The effect of a factor could be isolated by setting all other factors to zero, so that the response value became
equal to the effect of the factor being isolated. In that way each effect was isolated and then modelled as a function of
the factor. Thus, the technique was developed, for modelling a response variable as a function of its input factors.
