Why use a neural net instead of a generalized regression?
Mathematical models are normally built by making a priori assumptions about the functional form of the solution. These are called parametric models, and are solved by regression methods to determine a number of coefficients. This is sufficient if you know that the solution must be a second order polynomial, or some other simple, well-known function. But in the real world, relationships are not necessarily simple. Inputs and outputs could even be related in a non-linear fashion. If you do not have to guess the functional form of the answer, you have a big advantage.
