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National Repository of Grey Literature 12 records found  previous11 - 12  jump to record: Search took 0.01 seconds. 
Stochastic policy in Q-lerning used for control of AMB
 Březina, Tomáš ; Krejsa, Jiří ; Věchet, S.
A great intention is lately focused on Reinforcement Learning (RL) methods. The article is focused on improving model free RL method known as Q-learning used on active magnetic bearing model. Stochastic strategy and adaptive integration step increased the speed of learning approximately hundred times. Impossibility of using proposed improvement online is the only drawback, however it might be used for pretraining and further fined online.
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Použití LWR kompenzátoru pro řízení aktivního magnetického ložiska
 Březina, Tomáš ; Pulchart, J. ; Krejsa, Jiří
The active magnetic bearing (AMB) control is a complex problem, as it is fast, unstable and nonlinear system. The contribution deals with the simulation verification of AMB control using conventional PSD controller with nonlinear forward compensator. The automated universal approximator based on locally weighted learning (namely Receptive Field Weighted Regression - RFWR method) is used as the compensator.
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National Repository of Grey Literature : 12 records found   previous11 - 12  jump to record:
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