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：大多数线性回归方程（LRE）未知参数的辨识方法在存在相加扰动的情况下仅能保证参数误差的有界性，这在实际应用中几乎始终难以接受。本文提出一种新的辨识律，以克服这一缺陷，并在所述相加扰动满足特殊平均条件下，保证未知参数估计误差渐近收敛至零。理论结果通过数值仿真加以验证。