# Relaxation of Conditions for Convergence of Dynamic Regressor Extension and Mixing Procedure

arXiv: https://arxiv.org/abs/2112.04548 · HTML: https://mlsanity.com/paper/2112.04548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Partial Excitation in Parameter Learning](https://mlsanity.com/paper/2503.02235/) (2025)

## Abstract (arXiv)

A generalization of the dynamic regressor extension and mixing procedure is proposed, which, unlike the original procedure, first, guarantees a reduction of the unknown parameter identification error if the requirement of regressor semi-finite excitation is met, and second, it ensures exponential convergence of the regression function (regressand) tracking error to zero when the regressor is semi-persistently exciting with a rank one or higher.