  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.
