  We consider two high-order tuners that have been shown to have accelerated
performance, one based on Polyak's heavy ball method and another based on
Nesterov's acceleration method. We show that parameter estimates are bounded
and converge to the true values exponentially fast when the regressors are
persistently exciting. Simulation results corroborate the accelerated
performance and accelerated learning properties of these high-order tuners in
comparison to algorithms based on normalized gradient descent.
