1. Method for creating a non-linear, stationary or dynamic model of a machine, in particular of a combustion engine or of a partial system thereof, over the entire area of all operating points of the machine, preferably using neuronal nets, and whereby the control variable is dependent on a set of input quantities, for example system parameters, system parameters differentiated by time, time-delayed output quantities with feedback and/or preset values at the respective operating point, and whereby the control variable for a group of operating points is determined by way of a measurement technique from the total space of operating points of the machine, an output quantity is determined per model function at these operating points using a simplified partial model function, and whereby at any operating point the output quantities of each partial model function are added in a weighted fashion to an associated weighting function to arrive at a total output quantity for the respective operating point, and whereby for all operating points with control variable determined by a measurement technique the difference, respectively, between the total output quantity and the value of the control value arrived at by a measurement technique is determined and in areas of operating points where the absolute value of this difference is above a preset value, a further model function with a further associated weighting function is used for which the absolute value of the difference stays below the preset value wherein the step of determining the difference between the total output quantity of the associated partial model functions and a value of the control value arrived at this operating point, that is evaluated by a measurement technique, and the step of the application of a further model function and further weighting function is repeated as many times as needed until the statistically evaluated prediction quality of the overall model has reached the desired value.