التفاصيل البيبلوغرافية
| العنوان: |
Regularization-Induced Bias and Consistency in Recursive Least Squares |
| المؤلفون: |
Lai, Brian, Islam, Syed Aseem Ul, Bernstein, Dennis S. |
| المصدر: |
2021 American Control Conference (ACC), 2021, pp. 3987-3992 |
| سنة النشر: |
2021 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Electrical Engineering and Systems Science - Systems and Control |
| الوصف: |
Within the context of recursive least squares (RLS) parameter estimation, the goal of the present paper is to study the effect of regularization-induced bias on the transient and asymptotic accuracy of the parameter estimates. We consider this question in three stages. First, we consider regression with random data, in which case persistency is guaranteed. Next, we apply RLS to finite-impulse-response (FIR) system identification and, finally, to infinite-impulse-response (IIR) system identification. For each case, we relate the condition number of the regressor matrix to the transient response and rate of convergence of the parameter estimates. |
| نوع الوثيقة: |
Working Paper |
| DOI: |
10.23919/ACC50511.2021.9482798 |
| URL الوصول: |
http://arxiv.org/abs/2106.08799 |
| رقم الانضمام: |
edsarx.2106.08799 |
| قاعدة البيانات: |
arXiv |