@misc{Niederliński_Antoni_A,
 author={Niederliński, Antoni},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={It is demonstrated that a recently derived bound on the error convergence rate in (open-loop) RLS estimation is applicable to RLS-based stochastic self-tuning control as well. This leads to a generalized upper bound for the estimation error convergence rate in stochastic self-tuning control. The bound is shown to converge to zero under some assumptions regarding the model structure. The result is used to formulate two principles of self-tuning stating sufficient conditions under which self-tuning to stability and self-tuning to parameter consistency may occur.},
 type={artykuł},
 title={A new approach to convergence analysis of RLS-based self-tuning stochastic control},
 keywords={sterowanie, sterowanie-teoria, sztuczna inteligencja, matematyka stosowana, informatyka},
}