  We describe an iterative unfolding method for experimental data, making use
of a regularization function. The use of this function allows one to build an
improved normalization procedure for Monte Carlo spectra, unbiased by the
presence of possible new structures in data. We unfold, in a dynamically stable
way, data spectra which can be strongly affected by fluctuations in the
background subtraction and simultaneously reconstruct structures which were not
initially simulated.
