Search for dissertations about: "Nonlinear estimation"

Showing result 1 - 5 of 188 swedish dissertations containing the words Nonlinear estimation.

  1. 1. Estimation of Nonlinear Dynamic Systems : Theory and Applications

    Author : Thomas B. Schön; Fredrik Gustafsson; Simon Godsill; Linköpings universitet; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Nonlinear estimation; system identification; Kalman filter; particle filter; marginalized particle filter; expectation maximization; automotive applications; Automatic control; Reglerteknik;

    Abstract : This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic systems. Sequential Monte Carlo methods are mainly used to this end. These methods rely on models of the underlying system, motivating some developments of the model concept. READ MORE

  2. 2. On computational methods for nonlinear estimation

    Author : Thomas Schön; Linköpings universitet; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; Nonlinear estimation; Particle filter; Kalman filter; System identification; Convex optimization; Differential-algebraic equation; TECHNOLOGY; TEKNIKVETENSKAP;

    Abstract : The Bayesian approach provides a rather powerful framework for handling nonlinear, as well as linear, estimation problems. We can in fact pose a general solution to the nonlinear estimation problem. However, in the general case there does not exist any closed-form solution and we are forced to use approximate techniques. READ MORE

  3. 3. Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions

    Author : Mohamed Abdalmoaty; Håkan Hjalmarsson; Adrian Wills; KTH; []
    Keywords : Prediction Error Method; Maximum Likelihood; Data-driven; Learning; Stochastic; Nonlinear; Dynamical Models; Non-stationary Linear Predictors; Intractable Likelihood; Latent Variable Models; Estimation; Process Disturbance; Electrical Engineering; Elektro- och systemteknik;

    Abstract : Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. READ MORE

  4. 4. Estimation of the In-Cylinder Air/Fuel Ratio of an Internal Combustion Engine by the Use of Pressure Sensors

    Author : Per Tunestål; Förbränningsmotorer; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; vakuumteknik; hydraulik; Maskinteknik; nonlinear; cylinder pressure; engine; least-squares; Mechanical engineering; estimation; hydraulics; vibration and acoustic engineering; vacuum technology; vibrationer; akustik; Motors and propulsion systems; Motorer; framdrivningssystem;

    Abstract : This thesis investigates the use of cylinder pressure measurements for estimation of the in-cylinder air/fuel ratio in a spark ignited internal combustion engine. An estimation model which uses the net heat release profile for estimating the cylinder air/fuel ratio of a spark ignition engine is developed. READ MORE

  5. 5. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors

    Author : Mohamed Abdalmoaty; Håkan Hjalmarsson; Jimmy Olsson; KTH; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; Stochastic Nonlinear Systems; Nonlinear System Identification; Learning Dynamical Models; Maximum Likelihood; Estimation; Process Disturbance; Prediction Error Method; Non-stationary Linear Predictors; Intractable Likelihood; Latent Variable Models; Electrical Engineering; Elektro- och systemteknik;

    Abstract : The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. READ MORE