Search for dissertations about: "Regressor selection"

Showing result 1 - 5 of 6 swedish dissertations containing the words Regressor selection.

  2. 1. Regressor and Structure Selection : Uses of ANOVA in System Identification

    Author : Ingela Lind; Lennart Ljung; Torsten Söderström; Linköpings universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; System identification; Regressor selection; Analysis of variance; Nonlinear systems; Structure selection; Automatic control; Reglerteknik;

    Abstract : Identification of nonlinear dynamical models of a black box nature involves both structure decisions (i.e., which regressors to use and the selection of a regressor function), and the estimation of the parameters involved. READ MORE

  4. 2. Regressor Selection in System Identification using ANOVA

    Author : Ingela Lind; Linköpings universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

    Abstract : The aim of this work is to nd a good method to select regressors for nonlinear system identification. A literature survey over possible methods to select the model structure for nonlinear systems, mainly autoregressive processes, is first given. The main ideas are:1. Compare estimated models, using different regressor vectors, with each other. READ MORE

  5. 3. Initialization Methods for System Identification

    Author : Christian Lyzell; Lennart Ljung; Martin Enqvist; Magnus Jansson; Linköpings universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; System identification; Initialization methods; Regressor selection; Identifiability; Automatic control; Reglerteknik;

    Abstract : 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. READ MORE

  6. 4. Non-Linear System Identification with Neural Networks

    Author : Jonas Sjöberg; Linköpings universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

    Abstract : This thesis addresses the non-linear system identification problem, and in particular, investigates the use of neural networks in system identification. An overview of different possible mode! structures is given in a common framework. READ MORE

  7. 5. Regularization for Sparseness and Smoothness : Applications in System Identification and Signal Processing

    Author : Henrik Ohlsson; Lennart Ljung; Jacob Roll; Bo Wahlberg; Linköpings universitet; []
    Keywords : Regularization; sparsity; smothness; lasso; l1; fMRI; bio-feedback; TECHNOLOGY; TEKNIKVETENSKAP;

    Abstract : In system identification, the Akaike Information Criterion (AIC) is a well known method to balance the model fit against model complexity. Regularization here acts as a price on model complexity. READ MORE