Convergence and Stability of Recursive Damped Least Square Algorithm
-
摘要: 递推最小二乘法是参数辨识中最常用的方法,但容易产生参数爆发现象.因此对一种更稳定的辨识方法——递推阻尼最小二乘法进行了收敛特性的分析.在使用算法之前先归一化测量向量,结果表明,参数化距离收敛于一个零均值随机变量,并且在持续激励条件下,适应增益矩阵的条件数有界.参数化距离的方差有界.Abstract: The recursive least square is widely used in parameter identification.But it is easy to bring about the phenomena of parameters burst-off.A convergence analysis of a more stable identification algorithm-recursive damped least square is proposed.This is done by normalizing the measurement vector entering into the identification algorithm.It is shown that the parametric distance converges to a zero mean random variable.It is also shown that under persistent excitation condition,the condition number of the adaptation gain matrix is bounded,and the variance of the parametric distance is bounded
-
Key words:
- system identification /
- damped least square /
- recursive algorithm /
- convergence /
- stability
-
[1] Goodwin G C.Adaptive Filtering Prediction and Control[M].Englewood Cliffs:Prentice Hall,1984. [2] Lozanno R.Convergence analysis of recursive identification algorithms with forgetting factor[J].Automatica,1983,19(1):95～97. [3] Ljung L.Analysis of a general recursive prediction error identification algorithm[J].Automatica,1981,17(1):89～99. [4] Spripada N R,Fisher D G.Improved least squares identification[J].Internat J Control,1987,46(6):1889～1913. [5] Levenberg K.A method for the solution of certain non-linear problems in least squares[J].Quart Appl Math,1944,26(2):164～168. -
计量
- 文章访问数: 3268
- HTML全文浏览量: 190
- PDF下载量: 1384
- 被引次数: 0
下载:
渝公网安备50010802005915号