Non-linear model with disturbance rejection. A method for training a non
linear model for predicting an output parameter of a system is disclosed
that operates in an environment having associated therewith slow varying
and unmeasurable disturbances. An input layer is provided having a
plurality of inputs and an output layer is provided having at least one
output for providing the output parameter. A data set of historical data
taken over a time line at periodic intervals is generated for use in
training the model. The model is operable to map the input layer through
a stored representation to the output layer. Training of the model
involves training the stored representation on the historical data set to
provide rejection of the disturbances in the stored representation.