Most identification methods 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. Theoretical results are illustrated by numerical simulations.

翻译：线性回归方程（LRE）未知参数的大多数辨识方法在存在加性扰动时仅能确保参数误差有界，这在实际场景中几乎不可接受。本文提出一种新的辨识律，以克服这一缺陷，并保证当所述加性扰动满足特定平均条件时，未知参数估计误差渐近收敛于零。理论结果通过数值仿真加以验证。