  A special aspect of parameter identification in finite-strain
elasto-plasticity is considered. Namely, we analyze the impact of the
measurement errors on the resulting set of material parameters. In order to
define the sensitivity of parameters with respect to the measurement errors, a
mechanics-based distance between two sets of parameters is introduced. Using
this distance function, we assess the reliability of certain parameter
identification procedures. The assessment involves introduction of artificial
noise to the experimental data; the noise can be both correlated and
uncorrelated. An analytical procedure to speed up Monte Carlo simulations is
presented. As a result, a simple tool for estimating the robustness of
parameter identification is obtained. The efficiency of the approach is
illustrated using a model of finite-strain elasto-plasticity, which accounts
for combined isotropic and kinematic hardening. It is shown that dealing with
correlated measurement errors, most stable identification results are obtained
for non-diagonal weighting matrix. At the same time, there is a conflict
between the stability and accuracy.
