Sökning: "nonlinear models"

Visar resultat 1 - 5 av 433 avhandlingar innehållade orden nonlinear models.

  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. 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

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

    Författare :Martin Enqvist; Bengt Carlsson; Linköpings universitet; []
    Nyckelord :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. Probabilistic Sequence Models with Speech and Language Applications

    Författare :Gustav Eje Henter; W. Bastiaan Kleijn; Arne Leijon; Gernot Kubin; KTH; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; Time series; acoustic modelling; speech synthesis; stochastic processes; causal-state splitting reconstruction; robust causal states; pattern discovery; Markov models; HMMs; nonparametric models; Gaussian processes; Gaussian process dynamical models; nonlinear Kalman filters; information theory; minimum entropy rate simplification; kernel density estimation; time-series bootstrap;

    Sammanfattning : Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. LÄS MER

  5. 5. 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