  Most identification laws of unknown parameters of linear regression equations
(LRE) ensure only boundedness of a parametric error in the presence of additive
perturbations, which is almost always unacceptable for practical scenarios. In
this paper, a new identification law is proposed to overcome this drawback and
guarantee asymptotic convergence of the unknown parameters estimation error to
zero in case the mentioned additive perturbation meets special averaging
conditions. Such law is successfully applied to state reconstruction problem.
Theoretical results are illustrated by numerical simulations.
