Searched for: subject%3A%22Model%255C+learning%22
(1 - 8 of 8)
document
Kubalík, Jiří (author), Derner, Erik (author), Babuska, R. (author)
Virtually all dynamic system control methods benefit from the availability of an accurate mathematical model of the system. This includes also methods like reinforcement learning, which can be vastly sped up and made safer by using a dynamic system model. However, obtaining a sufficient amount of informative data for constructing dynamic...
journal article 2021
document
Derner, Erik (author), Kubalík, Jiří (author), Ancona, N. (author), Babuska, R. (author)
Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit...
journal article 2020
document
Kubalik, Jiai (author), Derner, Erik (author), Babuska, R. (author)
In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then...
conference paper 2020
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van Bekkum, Rob (author)
Decision-theoretic planning techniques are increasingly being used to obtain (optimal) plans for domains involving uncertainty, which may be present in the form of the controlling agent's actions, its percepts, or exogenous factors in the domain. These techniques build on detailed probabilistic models of the underlying system, for which Markov...
master thesis 2017
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Rastogi, Divyam (author)
Reinforcement Learning (RL) is a general purpose framework for designing controllers for non-linear systems. It tries to learn a controller (policy) by trial and error. This makes it highly suitable for systems which are difficult to control using conventional control methodologies, such as walking robots. Traditionally, RL has only been...
master thesis 2017
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Rogalla, M.J. (author)
Modern software is becoming more and more complex and manual testing cannot keep up with the need for high-quality reliable software: often due to the complexity of event-driven software, manual testing is done. This comes with many disadvantages in comparison with automated testing. The increased importance of having a secure, reliable online...
master thesis 2017
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Grondman, I. (author)
Classical control theory requires a model to be derived for a system, before any control design can take place. This can be a hard, time-consuming process if the system is complex. Moreover, there is no way of escaping modelling errors. As an alternative approach, there is the possibility of having the system learn a controller by itself while...
doctoral thesis 2015
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Harahap, G. (author)
Improving the performance of Automated Speech Recognition system requires incorporating more knowledge in the model of Automated Speech Recognition system. Information such as the context of the conversation and the characteristics of the speaker can make the task of recognizing speech more accurate. The challenge is how this knowledge can be...
master thesis 2010
Searched for: subject%3A%22Model%255C+learning%22
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