"ARX model; cautious strategy; stochastic LQ control; monte carlo approach"@en . . "Stochastick\u00E9 diskr\u00E9tn\u00ED modely jsou obvykl\u00FDm n\u00E1strojem pro popis neur\u010Dit\u00FDch syst\u00E9m\u016F. Tento p\u0159\u00EDsp\u011Bvek porovn\u00E1v\u00E1 dv\u011B optim\u00E1ln\u00ED \u0159\u00EDd\u00EDc\u00ED strategie: opatrnou a d\u016Fv\u011B\u0159ivou LQ optim\u00E1ln\u00ED strategii \u0159\u00EDzen\u00ED ARX modelu. V obou p\u0159\u00EDpadech je minimalizov\u00E1na st\u0159edn\u00ED hodnota krit\u00E9ria. Pokud respektujeme v\u0161echny neur\u010Ditosti v syst\u00E9mu a pou\u017Eije se optim\u00E1ln\u00ED strategie \u0159\u00EDzen\u00ED, pravd\u011Bpodobnostn\u00ED rozd\u011Blen\u00ED v\u00FDsledn\u00E9ho krit\u00E9ria je komplikovan\u00E9, ale jeho tvar m\u016F\u017Ee b\u00FDt z\u00EDsk\u00E1n metodami Monte Carlo. Hlavn\u00EDm c\u00EDlem tohoto p\u0159\u00EDsp\u011Bvku je odhadnout tvar rozd\u011Blen\u00ED krit\u00E9ria."@cs . . . "Havlena, Vladim\u00EDr" . "Stochastic discrete time models are usual tools used for description of uncertain systems. In the paper two optimal control strategies are compared: cautious and certainty equivalent LQ optimal control strategies of ARX model. In all cases the mean value of the criterion is minimized. When all uncertainties in the system are respected and optimal control strategies are used, the probability distribution of the criterion is complicated but its form can be obtained only by Monte Carlo approach. The main goal of the paper is to estimate the form of the criterion distribution."@en . "978-0-88986-781-9" . . "Acta Press" . "6"^^ . . "21230" . "Porovn\u00E1n\u00ED stochasticky optim\u00E1ln\u00EDch \u0159\u00EDdic\u00EDch strategi\u00ED - Monte Carlo p\u0159\u00EDstup"@cs . "Stochastic discrete time models are usual tools used for description of uncertain systems. In the paper two optimal control strategies are compared: cautious and certainty equivalent LQ optimal control strategies of ARX model. In all cases the mean value of the criterion is minimized. When all uncertainties in the system are respected and optimal control strategies are used, the probability distribution of the criterion is complicated but its form can be obtained only by Monte Carlo approach. The main goal of the paper is to estimate the form of the criterion distribution." . . . "Porovn\u00E1n\u00ED stochasticky optim\u00E1ln\u00EDch \u0159\u00EDdic\u00EDch strategi\u00ED - Monte Carlo p\u0159\u00EDstup"@cs . . . "Comparison of Stochastic Optimal Control Strategies - Monte Carlo Approach" . "Rathousk\u00FD, Jan" . "3"^^ . . "RIV/68407700:21230/09:03154642" . "Comparison of Stochastic Optimal Control Strategies - Monte Carlo Approach" . "3"^^ . . . "P(GA102/08/0442)" . . "Calgary" . "\u0160techa, Jan" . . "Comparison of Stochastic Optimal Control Strategies - Monte Carlo Approach"@en . "Innsbruck" . "Proceedings of the 28th IASTED International Conference on Modelling, Identification and Control" . . . "[B6696D8B997A]" . "2009-02-16+01:00"^^ . . "Comparison of Stochastic Optimal Control Strategies - Monte Carlo Approach"@en . "RIV/68407700:21230/09:03154642!RIV09-GA0-21230___" . . . "307784" .