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Showing 1 to 12 of 12 for “"Least squares minimization"”.
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Production of Ultra-Thick Sodium Nitroprusside Absorbers for the Determination of Mössbauer Transmission Integral Parameters.
… of absorbers are fitted simultaneously under a least squares minimization criterion to the transmission integral. This technique, when applied to 151Eu (source) and EU2O3 (absorber) to determine their recoil-free fractions, produced internally consistent values for these parameters. However, …
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A high-order Legendre-WENO numerical scheme for the non-conservative kernel density equations modeling particle-laden flows
… flows. The kernel density equations arise from least squares minimization of the approximation of the position-velocity phase-space particle distribution by a sum of kernel density functions. For concreteness in this work, Gaussian kernel density functions are used in approximating the particle …
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An improved size, matching, and scaling synthesis method for the design of meso-scale truss structures
… in this method: constrained optimization and least-squares minimization, constrained minimization converges faster, but least-squares minimization yields slightly improved performance results. In addition to these algorithms, a one-variable approach using an untested, simplifying assumption, …
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Kernel-based Lagrangian method for imperfectly-mixed chemical reactions, A
… simulations with a kernel half-width given by least squares minimization perform better than those made to match at one specific time. A heuristic time-variable kernel size, based on the previous results, performs on par with the least squares fixed kernel size.
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Improvements to power system dynamic load model parameter estimation
… system identification methods based on least-squares minimization have difficulty uniquely identifying the model parameters because the models exhibit parameter insensitivity and interdependency: vastly different model parameters can produce the same output waveform for a given …
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Robust Adaptive Signal Processors
… signal processing algorithms derived from the least squares minimization criterion require estimates of the N-dimensional input interference and noise statistics. Often, estimated statistics are biased by contaminant data (such as outliers and non-stationary data) that do not fit the dominant …
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Information Geometry For Nonlinear Least-Squares Data Fitting And Calculation Of The Superconducting Superheating Field
… explore the information geometric properties of least squares data fitting, particularly for so-called "sloppy" models. Second we describe a calculation of the superconducting superheating field, relevant for advancing gradients in particle accelerator resonance cavities. Parameter estimation by …
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Modeling contaminant spread and mitigation in the indoor environment
… and determine them by minimizing the sum of the squares of relative error between the calculated and experimental ventilation rates for the whole facility; the other is to assume that the crack areas are independent of each other and a similar least-squares minimization is applied to determine …
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Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices
… in fact reducing to finite-dimensional linear least-squares minimization. We show how to make this solution robust to model perturbation, sampled data, and measurement noise, as well as provide a recursive implementation for online learning. Finally, we apply our new formulation of inverse …
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Robust techniques and applications in fuzzy clustering
… issue of sensitivity to noise and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness …
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Gauss-newton Based Learning For Fully Recurrent Neural Networks
… the specific optimization problem, (non-linear least squares), which aims to speed up the process of FRNN training. The new approach stands as a robust and effective compromise between the original gradient-based RTRL (low computational complexity, slow convergence) and Newton-based variants of …
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The electron density : a bridge between exact quantum mechanics and fuzzy chemical concepts
… states, that when di®erent solutions to the least squares minimization problem are available with about the same statistical measures of quality and with about the same residual density, then the solution is to prefer, which yields a minimum density at the bond critical point (BCP) and a …