Convergence and Stability of Recursive Damped Least Square Algorithm
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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
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Key words:
- system identification /
- damped least square /
- recursive algorithm /
- convergence /
- stability
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