Sökning: "Thomas Schön"
Visar resultat 11 - 15 av 46 avhandlingar innehållade orden Thomas Schön.
11. Sequential Monte Carlo for inference in nonlinear state space models
Sammanfattning : Nonlinear state space models (SSMs) are a useful class of models to describe many different kinds of systems. Some examples of its applications are to model; the volatility in financial markets, the number of infected persons during an influenza epidemic and the annual number of major earthquakes around the world. LÄS MER
12. Identification using Convexification and Recursion
Sammanfattning : System identification studies how to construct mathematical models for dynamical systems from the input and output data, which finds applications in many scenarios, such as predicting future output of the system or building model based controllers for regulating the output the system.Among many other methods, convex optimization is becoming an increasingly useful tool for solving system identification problems. LÄS MER
13. Improved diagnosis and prediction of community-acquired pneumonia
Sammanfattning : Community-acquired pneumonia (CAP) is a major cause of morbidity and mortality worldwide. Although there is wide variation in the microbial etiology, CAP may manifest with similar symptoms, making institution of proper treatment challenging. LÄS MER
14. Dual control concepts for linear dynamical systems
Sammanfattning : We study simultaneous learning and control of linear dynamical systems. In such a setting, control policies are derived with respect to two objectives: i) to control the system as well as possible, given the current knowledge of system dynamics (exploitation), and ii) to gather as much information as possible about the unknown system that can improve control (exploration). LÄS MER
15. Robust learning and control of linear dynamical systems
Sammanfattning : We consider the linear quadratic regulation problem when the plant is an unknown linear dynamical system. We present robust model-based methods based on convex optimization, which minimize the worst-case cost with respect to uncertainty around model estimates. LÄS MER