Can dynamic adaptation gain speed up recursive least squares algorithm?

Dynamic adaptation gain/learning rate have been introduced in the context of adaptation/learning algorithms using scalar adaptation gains/learning rates in order to accelerate the adaptation transients. This paper shows by means of theoretical analysis, simulations and experimental results (on an active noise control system) that inserting a dynamic adaptation gain into the recursive least squares algorithm speeds up the adaptation transients in a deterministic environment and the asymptotic convergence in the stochastic case.