Search for dissertations about: "nonlinear regression"
Showing result 16 - 20 of 49 swedish dissertations containing the words nonlinear regression.
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16. Estimation of Nonlinear Greybox Models for Marine Applications
Abstract : As marine vessels are becoming increasingly autonomous, having accurate simulation models available is turning into an absolute necessity. This holds both for facilitation of development and for achieving satisfactory model-based control. READ MORE
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17. Subspace Selection Techniques for Classification Problems
Abstract : The main topic of this thesis is linear subspaces for regression - how to find the subspaces and how to evaluate them. The motivation to do regression in a subspace is numerical as well as computational - numerical in the sense that the subspace can filter out the relevant components or features of the problem, computationally in the sense that this filtering can be done quickly and then can nonlinear predictionby artificial neural networks, for instance, be conducted in lower dimensionality. READ MORE
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18. Nonlinear modeling of reinforced dowel joints in timber structures : a combined experimental-numerical study
Abstract : Steel dowels are indispensable elements for the design of joints in modern timber structures. Dowels are broadly used because of their flexibility in design and easy assembling on-site, as well as due to their advantageous mechanical behavior. READ MORE
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19. Local and weighted regression. Bias reduction and model validation
Abstract : Nonlinear systems might be estimated, using local linear models. If the estimation data is corrupted by strongly colored noise the local model will have a bias error. In linear system identification the bias error can be reduced by using instrumentalvariable methods. READ MORE
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20. Convex Optimization for Assignment and Generalized Linear Regression Problems
Abstract : This thesis considers optimization techniques with applications in assignment and generalized linear regression problems. The first part of the thesis investigates the worst-case robust counterparts of combinatorial optimization problems with least squares (LS) cost functions, where the uncertainty lies on the linear transformation of the design variables. READ MORE