Sökning: "Nonlinear System Identification"
Visar resultat 6 - 10 av 98 avhandlingar innehållade orden Nonlinear System Identification.
6. Initialization Methods for System Identification
Sammanfattning : In the system identification community a popular framework for the problem of estimating a parametrized model structure given a sequence of input and output pairs is given by the prediction-error method. This method tries to find the parameters which maximize the prediction capability of the corresponding model via the minimization of some chosen cost function that depends on the prediction error. LÄS MER
7. Nonlinear System Identification and Control Applied to Selective Catalytic Reduction Systems
Sammanfattning : The stringent regulations of emission levels from heavy duty vehicles create a demand for new methods for reducing harmful emissions from diesel engines. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using a selective catalyst as an aftertreatment system, utilising ammonia (NH3) for its reduction. LÄS MER
8. On risk-coherent input design and Bayesian methods for nonlinear system identification
Sammanfattning : System identification deals with the estimation of mathematical models from experimental data. As mathematical models are built for specific purposes, ensuring that the estimated model represents the system with sufficient accuracy is a relevant aspect in system identification. LÄS MER
9. Inference techniques for stochastic nonlinear system identification with application to the Wiener-Hammerstein models
Sammanfattning : Stochastic nonlinear systems are a specific class of nonlinear systems where unknown disturbances affect the system's output through a nonlinear transformation. In general, the identification of parametric models for this kind of systems can be very challenging. LÄS MER
10. Linear Models of Nonlinear Systems
Sammanfattning : Linear time-invariant approximations of nonlinear systems are used in many applications and can be obtained in several ways. For example, using system identification and the prediction-error method, it is always possible to estimate a linear model without considering the fact that the input and output measurements in many cases come from a nonlinear system. LÄS MER