Search for dissertations about: "Stochastic Nonlinear Systems"
Showing result 1 - 5 of 53 swedish dissertations containing the words Stochastic Nonlinear Systems.
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1. Linear Models of Nonlinear Systems
Abstract : 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. READ MORE
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2. Estimation and Control of Resonant Systems with Stochastic Disturbances
Abstract : 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. READ MORE
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3. Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions
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
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4. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
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
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5. Inference techniques for stochastic nonlinear system identification with application to the Wiener-Hammerstein models
Abstract : 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. READ MORE