Chen Zengqiang, Lin Maoqiong, Yuan Zhuzhi. Convergence and Stability of Recursive Damped Least Square Algorithm[J]. Applied Mathematics and Mechanics, 2000, 21(2): 209-214.
Citation:
Chen Zengqiang, Lin Maoqiong, Yuan Zhuzhi. Convergence and Stability of Recursive Damped Least Square Algorithm[J]. Applied Mathematics and Mechanics, 2000, 21(2): 209-214.
Chen Zengqiang, Lin Maoqiong, Yuan Zhuzhi. Convergence and Stability of Recursive Damped Least Square Algorithm[J]. Applied Mathematics and Mechanics, 2000, 21(2): 209-214.
Citation:
Chen Zengqiang, Lin Maoqiong, Yuan Zhuzhi. Convergence and Stability of Recursive Damped Least Square Algorithm[J]. Applied Mathematics and Mechanics, 2000, 21(2): 209-214.
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