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 13 of 13 for “"RKHS"”.
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Signal detection in fractional Gaussian noise and an RKHS approach to robust detection and estimation
This thesis is divided into two parts. In the first part, the problem of signal detection in fractional Gaussian noise is considered. To facilitate the study of this problem, several results related to the reproducing kernel Hilbert space of fractional Brownian motion are presented. In particular, …
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Convergence of Kernel Methods for Modeling and Estimation of Dynamical Systems
… methods, the reproducing kernel Hilbert space (RKHS) embedding method and the empirical-analytical Lagrangian (EAL) model. RKHS embedding is a non-parametric extension of the classical adaptive estimation method that embeds the uncertain function in an RKHS, an infinite-dimensional function …
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Design and Simulation of a Model Reference Adaptive Control System Employing Reproducing Kernel Hilbert Space for Enhanced Flight Control of a Quadcopter
… of reproducing kernel Hilbert spaces (RKHSs) into the model reference adaptive control (MRAC) framework to enhance the control systems of quadcopters. Traditional MRAC systems, while robust under predictable conditions, can struggle with the dynamic uncertainties typical in unmanned …
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Data-Driven, Non-Parametric Model Reference Adaptive Control Methods for Autonomous Underwater Vehicles
… control using reproducing kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input systems. The first controller design is a model reference adaptive controller (MRAC) based on a vector- valued RKHS that is induced by operator-valued kernels. This paper formulates a …
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Modeling and Estimation of Linear and Nonlinear Piezoelectric Systems
… that uses reproducing kernel Hilbert space (RKHS) embedding methods, can estimate the underlying nonlinear function that governs the system's dynamics. A model built by such a method can overcome some of the limitations of the modeling approaches mentioned above. This dissertation discusses …
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Modeling, Approximation, and Control for a Class of Nonlinear Systems
… the plant in a reproducing kernel Hilbert space (RKHS), $H$. Furthermore, the well-posedness of the framework in the new formulation is established. We derive the sufficient conditions for existence, uniqueness, and stability of an infinite dimensional adaptive estimation problem. A condition for …
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Nonparametric sparse learning of dynamical systems
… operators in reproducing kernel Hilbert spaces (RKHS). Compared with methods using fixed parametric structures, the proposed nonparametric representation does not require manually engineered features, and the model grows and adjusts with the amount of training data, making it appealing from a …
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Advancing 6DoF Object Pose Estimation: Keypoint Voting, Optimal Keypoint Sampling, and Bridging the Simulation-to-real Gap
… the single-to-multiobject training gap. Finally, RKHSPose addresses the simulation-to-real domain gap through a self-supervised framework using a learnable kernel in RKHS and an adapter network pre-trained on synthetic data. This approach achieves competitive results against fully supervised …
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Robust Statistical Modeling In Functional Linear Regression
… in partial functional linear models under RKHS framework. The theoretical properties of robust estimation simulation studies are discussed in this chapter. Furthermore, two real data examples are presented to illustrate the performance of the robust procedure. Then, we extend three robust …
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Modeling and Estimation of Motion Over Manifolds with Motion Capture Data
… utilizes reproducing kernel Hilbert space (RKHS) formulations and Koopman theory, to achieve some of the advantages of learning theory for IID discrete systems to estimates generated over dynamical systems. Specifically, rates of convergence are determined for estimates generated via …
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Stochastic Optimization For Multi-Agent Statistical Learning And Control
… algorithm in reproducing kernel Hilbert spaces (RKHS) that ameliorates this complexity issue while preserving optimality: we combine the functional generalization of stochastic gradient method (FSGD) with greedily constructed low-dimensional subspace projections based on matching pursuit. We …
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Models and methods for computer simulations as a resource in plant breeding
… We proposed a new nonparametric method, pRKHS, which combined the features of supervised principal component analysis and reproducing kernel Hilbert spaces regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Compared to RR-BLUP, BayesA, BayesB, …
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Calibration of Option Pricing in Reproducing Kernel Hilbert Space
A parameter used in the Black-Scholes equation, volatility, is a measure for variation of the price of a financial instrument over time. Determining volatility is a fundamental issue in the valuation of financial instruments. This gives rise to an inverse problem known as the calibration problem …