  This thesis is devoted to the study of some aspects of perturbative QCD, and
in particular to the development of high-precision techniques for the
extraction of physical parameters such as structure functions, parton
distributions, and the strong coupling from the analysis of deep inelastic
scattering data. First, we will discuss scaling violations of singlet and
nonsinglet truncated moments, and the use of truncated momets to solve the
Altarelli-Parisi equation. Then we will suggest an approach based on neural
networks to the parametrization and interpolation of experimental data, which
retains information on experimental errors and correlations. The method of
truncated moments can be combined with the neural network fit to extract
various quantities of phenomenological interest in a bias-free way. As an
example of such application, we will discuss the determination of the strong
coupling constant.
