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	Control of Nonlinear Systems using Local Approximation Methods Brablc, Martin ; Bugeja, Marvin (oponent) ; Grepl, Robert (vedoucí práce) This thesis deals with the development of an adaptive control algorithm for a specific class of electromechanical actuators, based on the feedforward compensation principle using an inverse dynamic model. The control algorithm's adaptability originates in a mechanism of learning the inverse dynamic model. This thesis focuses on using local approximation methods for an online inverse model learning. The outcome of this thesis is a summary of the analysis, simulations and actual experiments, which tested the possibilities of using the local approximation methods for adaptive control purposes in real environment. Úplný záznam
	Control of Nonlinear Systems using Local Approximation Methods Brablc, Martin ; Bugeja, Marvin (oponent) ; Grepl, Robert (vedoucí práce) This thesis deals with the development of an adaptive control algorithm for a specific class of electromechanical actuators, based on the feedforward compensation principle using an inverse dynamic model. The control algorithm's adaptability originates in a mechanism of learning the inverse dynamic model. This thesis focuses on using local approximation methods for an online inverse model learning. The outcome of this thesis is a summary of the analysis, simulations and actual experiments, which tested the possibilities of using the local approximation methods for adaptive control purposes in real environment. Úplný záznam
	Lazy Learning of Environment Model from the Past Štěch, J. ; Guy, Tatiana Valentine ; Pálková, B. ; Kárný, Miroslav The paper addresses a lazy learning (LL) approach to decision making (DM) problem described in fully probabilistic way. The key idea of LL is to simplify the actual DM problem by using past DM problems similar to the current one. The approach can decrease computation complexity and increase quality of learning when no rich alternative information available. The proposed LL approach helps to learn the environment model based on a proximity of the past and current DM problem with Kullback-Leibler divergence serving as a proximity measure. The implemented algorithm is verified on the real data. The results show that the proposed approach improves prediction quality. Úplný záznam

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