Sökning: "Cristian R. Rojas"
Visar resultat 1 - 5 av 10 avhandlingar innehållade orden Cristian R. Rojas.
1. Module identification in dynamic networks: parametric and empirical Bayes methods
Sammanfattning : The purpose of system identification is to construct mathematical models of dynamical system from experimental data. With the current trend of dynamical systems encountered in engineering growing ever more complex, an important task is to efficiently build models of these systems. LÄS MER
2. 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
3. Consistency and efficiency in continuous-time system identification
Sammanfattning : Continuous-time system identification deals with the problem of building continuous-time models of dynamical systems from sampled input and output data. In this field, there are two main approaches: indirect and direct. In the indirect approach, a suitable discrete-time model is first determined, and then it is transformed into continuous-time. LÄS MER
4. Continuous-time System Identification : Refined Instrumental Variables and Sampling Assumptions
Sammanfattning : Continuous-time system identification deals with the problem of building continuous-time models of dynamical systems from sampled input and output data. There are two main approaches in this field: indirect and direct. In the indirect approach, a suitable discrete-time model is first determined, and then it is transformed into continuous-time. LÄS MER
5. Low-rank optimization in system identification
Sammanfattning : In this thesis, the use of low-rank approximations in connection with problems in system identification is explored. Firstly, the motivation of using low-rank approximations in system identification is presented and the framework for low-rank optimization is derived. LÄS MER
