Results 261 to 270 of about 1,591 (287)

On the Interpretation of Least Squares Collocation

1976
One of the central issues in current geodetic data reduction is the question of how noise-corrupted measurements of physical parameters, derived from two independent measurement processes, can be combined to obtain an optimal estimate of the parameters.
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Regularization by Least-Squares Collocation

1983
The term “least-squares collocation” (abbreviated “LSC” in the sequel) appears in many different contexts, where functions have to be approximated by terms resulting from finitely many measurements.
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On estimating gravity anomalies— A comparison of least squares collocation with conventional least squares techniques

Bulletin Géodésique, 1977
The least squares collocation algorithm for estimating gravity anomalies from geodetic data is shown to be an application of the well known regression equations which provide the mean and covariance of a random vector (gravity anomalies) given a realization of a correlated random vector (geodetic data).
P. Argentiero, B. Lowrey
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Astrogravimetric Leveling by Least Squares Collocation

The Canadian Surveyor, 1977
The application of least squares collocation to astrogravimetric leveling is described and the fundamental equation of astrogravimetric leveling in least squares collocation is derived. This method is easier and quicker to use than classical astrogravimetric leveling and is therefore well suited for extensive application.
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Least-squares collocation with covariance-matching constraints

Journal of Geodesy, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A least-squares preconditioner for radial basis functions collocation methods

Advances in Computational Mathematics, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Leevan Ling, Edward J. Kansa
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Development and Application of the Collocations and Least Squares Method

2009
The collocations and least squares (CLS) method is a projection method including the least squares algorithm. A computational domain is covered by a grid in the method. An approximate solution is found as a linear combination of basis functions in each cell of the grid in the CLS method. One can use different bases.
Vadim Isaev, Vasily Shapeev
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Least‐square collocation and Lagrange multipliers forTaylor meshless method

Numerical Methods for Partial Differential Equations, 2018
A recently proposed meshless method is discussed in this article. It relies on Taylor series, the shape functions being high degree polynomials deduced from the Partial Differential Equation (PDE). In this framework, an efficient technique to couple several polynomial approximations has been presented in (Tampango, Potier‐Ferry, Koutsawa, Tiem, Int. J.
Yang, Jie, Hu, Heng, Potier‐ferry, Michel   +2 more
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Prediction of Polar Motion by Least-Squares Collocation

1990
In this paper a procedure for predicting of pole positions using the least squares collocation approach is presented. Predicted pole coordinates are needed for a nearly “real-time” positioning. The main purpose of this work was to elaborate an efficient algorithm to evaluate x,y coordinates of the future pole positions, which would be as close as ...
Roman Galas, Rudolf Sigl
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Using line averages in least-squares collocation

Bulletin Géodésique, 1989
With the acquisition of gravimetric measurements by automated, moving-base systems, the data are in the form of averages over a line on or parallel to the earth's surface. In order to apply least-squares collocation to these data, a one-dimensional smoothing of the (two-dimensional) covariance function is required.
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