  We introduce a new model of linear regression for random functional inputs
taking into account the first order derivative of the data. We propose an
estimation method which comes down to solving a special linear inverse problem.
Our procedure tackles the problem through a double and synchronized
penalization. An asymptotic expansion of the mean square prevision error is
given. The model and the method are applied to a benchmark dataset of
spectrometric curves and compared with other functional models.
