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Approximation methods in fully probabilistic design of decision making under incomplete knowledge (master thesis)
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SYSNO ASEP 0093101 Document Type D - Thesis R&D Document Type The record was not marked in the RIV Title Approximation methods in fully probabilistic design of decision making under incomplete knowledge (master thesis) Title Metody aproximace plně pravděpodobnostního návrhu rozhodování za neúplné znalosti (diplomová práce) Author(s) Pištěk, Miroslav (UTIA-B) RID, ORCID Issue data Praha: MFF UK, 2007 Number of pages 47 s. Language eng - English Country CZ - Czech Republic Keywords dual control ; fully probabilistic design ; high dimensional model representations (HDMR) ; approximative solution of integral equations Subject RIV BC - Control Systems Theory R&D Projects 1ET100750401 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) 2C06001 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation In this thesis, we introduce an efficient algorithm for an optimal decision strategy approximation. It approximates the optimal equations of dynamic programming without omitting the principal uncertainty stemming from an uncomplete knowledge of a controlled system. Thus, the algorithm retains the ability to constantly verify the actual knowledge, which is the essence of dual control. An integral part of solution proposed is a reduction of memory demands using HDMR approximation. We have developed a general method for numerical solution of linear integral equations based on this approximation, and applied it to solve a linearized variant of optimal equations. To achieve such a variant, it was necessary to apply a different control design called fully probabilistic design which allows easier finding of a linearized approximation. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2008
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