Search for dissertations about: "Learning Dynamical Models"
Showing result 1 - 5 of 22 swedish dissertations containing the words Learning Dynamical Models.
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1. 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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2. Learning from Interactions : Forward and Inverse Decision-Making for Autonomous Dynamical Systems
Abstract : Decision-making is the mechanism of using available information to generate solutions to given problems by forming preferences, beliefs, and selecting courses of action amongst several alternatives. In this thesis, we study the mechanisms that generate behavior (the forward problem) and how their characteristics can explain observed behavior (the inverse problem). READ MORE
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3. Reinforcement Learning and Dynamical Systems
Abstract : This thesis concerns reinforcement learning and dynamical systems in finite discrete problem domains. Artificial intelligence studies through reinforcement learning involves developing models and algorithms for scenarios when there is an agent that is interacting with an environment. READ MORE
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4. Bayesian learning of structured dynamical systems
Abstract : In this thesis, we propose some Bayesian approaches to the identificationof structured dynamical systems. In particular, we consider block-orientedmodels in which a complex system is built starting from simple linear andnonlinear building blocks. READ MORE
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5. 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