Sökning: "Black-Box Optimization"
Visar resultat 16 - 20 av 29 uppsatser innehållade orden Black-Box Optimization.
16. Neural Network Approaches for Model Predictive Control
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Model Predictive Control (MPC) is an optimization-based paradigm forfeedback control. The MPC relies on a dynamical model to make predictionsfor the future values of the controlled variables of the system. LÄS MER
17. ERA: Evolution of Residual Architectures
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : This thesis investigates how well a neural architecture search can find competitive image classifiers on the CIFAR-10 data set with limited computational resources. Most work done on architecture search either uses vast computational resources or narrow and strongly informed space of possible solutions. LÄS MER
18. Study of a generation capacity expansion on an island
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The study carried out in this master thesis is part of a larger project led by Energynautics GmbH focusing on renewable energy development in the Caribbean. One of the Caribbean states, consisting of multiple islands, has set a target of 30 % of renewable energy in the power sector by 2030. LÄS MER
19. Distributed Bayesian Optimization in Multi-Agent Systems
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : A variety of engineering problems require extremely resource consuming system performance optimization with an inaccessible system model, some examples include tuning the hyper-parameters in a complex machine learning model, simulation-based aerodynamic design and power system optimization. Bayesian optimization is an approach to solve black-box optimization problems when sample efficiency is of high priority. LÄS MER
20. Automatic parameter tuning in localization algorithms
Master-uppsats, Linköpings universitet/Programvara och systemSammanfattning : Many algorithms today require a number of parameters to be set in order to perform well in a given application. The tuning of these parameters is often difficult and tedious to do manually, especially when the number of parameters is large. It is also unlikely that a human can find the best possible solution for difficult problems. LÄS MER
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