Search for dissertations about: "numerical method, linear system"

Showing result 11 - 15 of 156 swedish dissertations containing the words numerical method, linear system.

  2. 11. Fast Numerical Techniques for Electromagnetic Problems in Frequency Domain

    Author : Martin Nilsson; Per Lötstedt; Abderrahmane Bendali; Uppsala universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Fast Multipole Method; Minimal Residual Interpolation; Sparse Approximate Inverse preconditioning; Method of Moments; fast solvers; iterative methods; multiple right-hand sides; error analysis; Numerical Analysis; Numerisk analys;

    Abstract : The Method of Moments is a numerical technique for solving electromagnetic problems with integral equations. The method discretizes a surface in three dimensions, which reduces the dimension of the problem with one. A drawback of the method is that it yields a dense system of linear equations. READ MORE

  4. 12. Modelling and simulation of turbulence subject to system rotation

    Author : Olof Grundestam; Arne Johansson; Sharath Girimaji; KTH; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Direct numerical simulations; least-squares method; turbulence model; nonlinear modelling; system rotation; streamline curvature; high-lift aerodynamics; Fluid mechanics; Strömningsmekanik;

    Abstract : Simulation and modelling of turbulent flows under influence of streamline curvature and system rotation have been considered. Direct numerical simulations have been performed for fully developed rotating turbulent channel flow using a pseudo-spectral code. The rotation numbers considered are larger than unity. READ MORE

  5. 13. Algorithms in data mining using matrix and tensor methods

    Author : Berkant Savas; Lars Eldén; Lieven De Lathauwer; Linköpings universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Volume; Minimization criterion; Determinant; Rank deficient matrix; Reduced rank regression; System identification; Rank reduction; Volume minimization; General algorithm; Handwritten digit classification; Tensors; Higher order singular value decomposition; Tensor approximation; Least squares; Tucker model; Multilinear algebra; Notation; Contraction; Tensor matricization; Newton s method; Grassmann manifolds; Product manifolds; Quasi-Newton algorithms; BFGS and L-BFGS; Symmetric tensor approximation; Local intrinsic coordinates; Global embedded coordinates; ; Numerical analysis; Numerisk analys;

    Abstract : In many fields of science, engineering, and economics large amounts of data are stored and there is a need to analyze these data in order to extract information for various purposes. Data mining is a general concept involving different tools for performing this kind of analysis. READ MORE

  6. 14. 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. 15. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors

    Author : Mohamed Abdalmoaty; Håkan Hjalmarsson; Jimmy Olsson; KTH; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; 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