Sökning: "Stochastic Nonlinear Systems"

Visar resultat 1 - 5 av 45 avhandlingar innehållade orden Stochastic Nonlinear Systems.

  1. 1. Linear Models of Nonlinear Systems

    Författare :Martin Enqvist; Lennart Ljung; Rik Pintelon; Linköpings universitet; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; linear models; nonlinear systems; system identification; stochastic processes; linearization; mean-square error; Automatic control; 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. Estimation and Control of Resonant Systems with Stochastic Disturbances

    Författare :Peter Nauclér; Torsten Söderström; Anders Robertsson; Uppsala universitet; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; vibration control; stochastic control; stochastic systems; feedforward control; Wiener filtering; Kalman filtering; wave separation; unbalance estimation; nonlinear regression; Automatic control; Reglerteknik;

    Sammanfattning : The presence of vibration is an important problem in many engineering applications. Various passive techniques have traditionally been used in order to reduce waves and vibrations, and their harmful effects. Passive techniques are, however, difficult to apply in the low frequency region. LÄS MER

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

    Författare :Mohamed Abdalmoaty; Håkan Hjalmarsson; Adrian Wills; KTH; []
    Nyckelord :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

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

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

    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

  5. 5. Inference techniques for stochastic nonlinear system identification with application to the Wiener-Hammerstein models

    Författare :Giuseppe Giordano; Chalmers University of Technology; []
    Nyckelord :NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; nonlinear systems; system identification; stochastic; Maximum Likelihood; Wiener-Hammerstein; Monte Carlo; Newton s method;

    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