1. A method for training a non linear model for predicting an output parameter of a system that operates in an environment having associated therewith time varying and unmeasurable disturbances, comprising the steps of: providing an input layer having a plurality of inputs; providing an output layer having at least one output for providing the output parameter; providing a data set of historical data taken over a time line at periodic intervals; mapping the input layer through a stored representation to the output layer; and training the stored representation with the historical data set to account for the effects of unmeasured disturbances.