Convergence and stability of recursive damped least square algorithm
A convergence analysis for a new identification algorithm (recursive damped least squares) is given. It is shown that under persistent excitation conditions, the parametric distance converges to a zero mean and bounded variance random variable. Under the same conditions, the condition number of the adaptation gain matrix is bounded and the variance of the parametric distance is bounded. The results are formulated in two theorems. The proof of theorem 1 is omitted, theorem 2 is completely proved.
scientific article; zbMATH DE number 1490126