Sökning: "nonlinear models"

Visar resultat 1 - 5 av 421 avhandlingar innehållade orden nonlinear models.

  1. 1. Linear Models of Nonlinear Systems

    Detta är en avhandling från Institutionen för systemteknik

    Författare :Martin Enqvist; Lennart Ljung; Rik Pintelon; [2005]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; linear models; nonlinear systems; system identification; stochastic processes; linearization; mean-square error; TECHNOLOGY Information technology Automatic control; TEKNIKVETENSKAP Informationsteknik Reglerteknik;

    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

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

    Detta är en avhandling från KTH Royal Institute of Technology

    Författare :Mohamed Abdalmoaty; Håkan Hjalmarsson; Adrian Wills; [2019]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; 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;

    Sammanfattning : 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. LÄS MER

  3. 3. Some Results on Linear Models of Nonlinear Systems

    Detta är en avhandling från Linköping, Sweden : Linköpings universitet

    Författare :Martin Enqvist; Bengt Carlsson; [2003]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; System identification; Linear models; Nonlinear systems; TECHNOLOGY; TEKNIKVETENSKAP;

    Sammanfattning : Linear time-invariant approximations of nonlinear systems are used in many a pplications. Such approximations can be obtained in many ways. LÄS MER

  4. 4. On Simplification of Models with Uncertainty

    Detta är en avhandling från Department of Automatic Control, Lund Institute of Technology (LTH)

    Författare :Lennart Andersson; [1999]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Linear matrix inequalities LMIs ; Integral quadratic constraints IQCs ; Linearization; Nonlinear Models; Uncertainty; Power systems; Robustness analysis; Error bounds; Model simplification; Model reduction; Automation; robotics; control engineering; Automatiska system; robotteknik; reglerteknik;

    Sammanfattning : Mathematical models are frequently used in control engineering for analysis, simulation, and design of control systems. Many of these models are accurate but may for some tasks be too complex. In such situations the model needs to be simplified to a suitable level of accuracy and complexity. LÄS MER

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

    Detta är en avhandling från Stockholm, Sweden : KTH Royal Institute of Technology

    Författare :Mohamed Abdalmoaty; Håkan Hjalmarsson; Jimmy Olsson; [2017]
    Nyckelord :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;

    Sammanfattning : 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. LÄS MER