a general thrust settings, i.e. a control law, that could be used in such framework. The idea of fitting the mass parameter of BADA on a few past points is used. Results on the accuracy of this generic control law in the vertical plane should be expected in a near future.

A common flaw in parametric approaches concerns the nominal values used for every parameters. As shown on figure \[fig:massEffect\], the effect of the mass parameter clearly shows the importance of tuning these parameters. As an example, 400 seconds after the takeoff, there is already a difference of approximately 250 flight levels between the minimal and the maximum mass which is enormous for an application like the TP. This can easily become a burden when the model is rather complex. To overcome this inherent difficulty, non-parametric approaches are studied in order to obtain an aircraft model from the past trajectories. Non-parametric approaches rely on machine learning and statistical inference: [@LeFablec1999] uses neural networks, [@M2R_Richard_ALLIGIER] uses genetic programming in order to learn the structure of the variables of a linear regression and [@M2R_GHASEMI] uses fuzzy regression with k-nearest neighbor. The main drawback of non-parametric approaches is that it requires lots of 