Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 58 for “"Uncertain Parameters"”.
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Stochastic Control for Systems With Uncertain Parameters
Made available in DSpace on 2014-12-12T20:54:32Z (GMT). No. of bitstreams: 1 7709132.pdf: 3062578 bytes, checksum: e1aa21be67a8a4e52715245a33d08348 (MD5) Previous issue date: 1976
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A Variational Approach to Estimating Uncertain Parameters in Elliptic Systems
… on safety and reliability, the need to quantify uncertainty in model outputs due to uncertainties in the model parameters becomes critical. However, the statistical characterization of the model parameters is rarely known. In this thesis, we propose a variational approach to solve the stochastic …
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Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters
… operate under parametric and external excitation uncertainties. The polynomial chaos approach has been shown to be more efficient than Monte Carlo approaches for quantifying the effects of such uncertainties on the system response. This work uses the polynomial chaos framework to develop new …
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A Polynomial Chaos Approach for Stochastic Modeling of Dynamic Wheel-Rail Friction
… demonstrated that CoF depends on various dynamic parameters and instantaneous conditions. In the real world, accurately estimating the CoF is difficult due to effects of various uncertain parameters, such as wheel and rail materials, rail roughness, contact patch, and so on. In this study, the …
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Multi-Mode Robust Appointment Scheduling for Uncertain Service Time and Random No-Show Using Min-Max Optimization
… services, healthcare, finance, and legal advice. Uncertainty of processing time and job no-shows make the problem more challenging. The majority of the literature now in existence makes unrealistic assumptions about most real-world scenarios, such as constant service time, and they use a vast …
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Efficient Identification and Control Methods For Nonlinear Systems Under Uncertainty
… and constraint handling capabilities under uncertainty. These methods are based on Moving Horizon Estimation (MHE), Nonlinear Model Predictive Control (NMPC), and Polynomial Chaos Theory (PCT).</p> <p>When implementing control on real multivariable chemical or petrochemical processes such as …
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Robust analysis and control of smart structural systems
… the systhesized controller has to be robust for uncertainties such as uncertain parameters and saturating actuators. In addition, the designed controller should have a lower order in order to simplify the hardware implementation. Different control methods are presented in this thesis to satisfy …
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Stochastic Modeling of Micro-Electromechanical Systems (Mems)
… is presented, which seeks to characterize uncertain input parameters based on available experimental information. This approach models the uncertain parameters as independent random variables, for which the distributions are estimated based on experimental observations, using a …
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Problem-driven scenario generation for stochastic programs
… mathematical programming in the presence of uncertainty. In a stochastic program uncertain parameters are modeled as random vectors and one aims to minimize the expectation, or some risk measure, of a loss function. However, stochastic programs are computationally intractable when the …
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Dynamic vegetation Roughness in the riparian zone
… flow characteristics and variation of vegetation parameters. The objective of this research was to develop and demonstrate techniques to hydraulically model an open channel river system while taking into account dynamic roughness due to vegetation. The Sedimentation and River Hydraulics in …
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A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion
Quantifying uncertainties in large-scale forward and inverse PDE simulations has emerged as a central challenge facing the field of computational science and engineering. The promise of modeling and simulation for prediction, design, and control cannot be fully realized unless uncertainties in …
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Parametric uncertainty analysis for complex engineering systems
… more complex. Given that there are inevitable uncertainties entering at every stage of a model's life cycle, the challenge is to identify those components that contribute most to uncertainties in the predictions. This thesis presents new methodologies for allowing direct incorporation of …
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Stochastic modeling and uncertainty quantification in microelectromechanical systems
Uncertainty quantification (UQ) has become a necessary step in the design of most modern engineering systems due to the need to create robust devices that can tolerate variations in the manufacturing process or in the operating environment. These variations or uncertainties can be represented by …
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Multi-parameter estimation in glacier models with adjoint and algorithmic differentiation
… which are still poorly understood. Input parameters, such as basal drag and topography, have large effects on the applied stress and flow fields but whose direct observation is very difficult, if not impossible. Computational methods are designed to aid in the development of ice sheet …
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Distributed Generation Capacity Assessment of Active Distribution Systems
… for the DG capacity. Next, we deal with the data uncertainty (e.g. load and DG output uncertainties) in the DG capacity assessment problem. To this end, we build our DG capacity assessment problem on the paradigm of robust optimisation (RO) methodology. In RO, the range of possible values for …
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Distributed Generation Capacity Assessment of Active Distribution Systems
… for the DG capacity. Next, we deal with the data uncertainty (e.g. load and DG output uncertainties) in the DG capacity assessment problem. To this end, we build our DG capacity assessment problem on the paradigm of robust optimisation (RO) methodology. In RO, the range of possible values for …
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Bayesian inference of stochastic dynamical models
… (DO) evolution equations for reduced-dimension uncertainty evolution and the Gaussian mixture model DO filtering algorithm for nonlinear reduced-dimension state variable inference to perform parallelized computation of marginal likelihoods for multiple candidate models, enabling efficient …
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Coping Uncertainty in Wireless Network Optimization
… networks, which requires knowledge of network parameters (e.g., channel state information). The majority of existing works assume that all network parameters are either given a prior or can be accurately estimated. However, in many practical scenarios, some parameters are uncertain at the time …
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Essays on Decision Making Under Uncertainty
… spaces, for solving an optimization problem with uncertain parameters for which we have historical data, including auxiliary data. The first method approximates the objective function and the second approximates the optimizer. We provide finite sample guarantees and prove asymptotic optimality for …
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Stability analysis of uncertain systems: Integral quadratic constraints approach
… respect to nonlinearities, time-variations and uncertain parameters called Intergral Quadratic Constraints (IQC) methodology. It can be used as an introduction to a specific technique developed by Alexandre Megretski and Anders Ranzer and as a manual to the software package for stability …
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