Sökning: "Identification Algorithm"
Visar resultat 11 - 15 av 225 avhandlingar innehållade orden Identification Algorithm.
11. Regression on Manifolds with Implications for System Identification
Sammanfattning : The trend today is to use many inexpensive sensors instead of a few expensive ones, since the same accuracy can generally be obtained by fusing several dependent measurements. It also follows that the robustness against failing sensors is improved. As a result, the need for high-dimensional regression techniques is increasing. LÄS MER
12. On performance analysis of subspace methods in system identification and sensor array processing
Sammanfattning : This thesis addresses the issue of performance analysis of subspace-based parameter estimation methods in two different applications, namely system identification and sensor array processing. The objective is to study the quality of the estimated models as the amount of data increases, and to suggest improvements and give user guidelines. LÄS MER
13. On Model Simplification in System Identification
Sammanfattning : This report deals with the connection between system identification and model reduction. We propose an identification algorithm that is based on the least squares identification method and either of the model reduction techniques: Frequency weighted truncated balanced realization or frequency weighted optimal Hankel-norm model reduction. LÄS MER
14. 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
15. Identification of Time Varying Systems and Application of System Identification to Signal Processing
Sammanfattning : Part IA new approach to identification of time varying systems is presented, and evaluated using computer simulations. The new approach is built upon the similarities between recursive least squares identification and Kalman filtering. LÄS MER