Results 111 to 120 of about 760,795 (164)
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2001
Least-squares is described as a method for solving problems where there is an excess of information available. This is often the case in experimental mechanics. The method can be used to include information from different sources; experimental and theoretical. The method is applied to curve fitting problems and to more general situations.
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Least-squares is described as a method for solving problems where there is an excess of information available. This is often the case in experimental mechanics. The method can be used to include information from different sources; experimental and theoretical. The method is applied to curve fitting problems and to more general situations.
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2011
In this chapter, we discuss the least squares/equation error techniques for parameter estimation, which are used for aiding the parameter estimation of dynamic systems (including algebraic systems), in general, and the aerodynamic derivatives of aerospace vehicles from the flight data, in particular.
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In this chapter, we discuss the least squares/equation error techniques for parameter estimation, which are used for aiding the parameter estimation of dynamic systems (including algebraic systems), in general, and the aerodynamic derivatives of aerospace vehicles from the flight data, in particular.
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Nonlinear Least‐Squares Fitting Methods
2008This chapter provides an overview of the techniques involved in "fitting equations to experimental data" with a particular emphasis on the what can be learned with these techniques, what are the requirements of the experimental data for these techniques, and what are the underlying assumptions of these techniques. The layout of this chapter is to start
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1981
In the next three chapters we shall discuss a particular form of statistical model, which gives rise to simple statistical methods of very wide applicability. The basic model has been mentioned in Section 2.1, Equation (2.2), see also Example 3.12, but the following example illustrates how it arises in practice.
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In the next three chapters we shall discuss a particular form of statistical model, which gives rise to simple statistical methods of very wide applicability. The basic model has been mentioned in Section 2.1, Equation (2.2), see also Example 3.12, but the following example illustrates how it arises in practice.
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2014
The method of least squares is the standard method for finding an approximate solution of a linear system with more equations than unknowns. Such systems occur in data fitting where one determines the fit by minimizing the sum of squares of the difference between the observed and the fitted values.
Tom Lyche, Jean-Louis Merrien
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The method of least squares is the standard method for finding an approximate solution of a linear system with more equations than unknowns. Such systems occur in data fitting where one determines the fit by minimizing the sum of squares of the difference between the observed and the fitted values.
Tom Lyche, Jean-Louis Merrien
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1995
In this Chapter we discuss techniques used to fit faulted current and voltage waveforms, each to a sinusoidal waveform containing a fundamental component, a decaying/constant DC component and/or harmonics. These techniques use the least squares (LSQ) method to minimise the fitting error, and all have the common goal of extracting the fundamental ...
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In this Chapter we discuss techniques used to fit faulted current and voltage waveforms, each to a sinusoidal waveform containing a fundamental component, a decaying/constant DC component and/or harmonics. These techniques use the least squares (LSQ) method to minimise the fitting error, and all have the common goal of extracting the fundamental ...
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2016
The method of least squares is the basic tool of developing and verifying models by fitting theoretical curves to data. Fitting functions that linearly depend on model parameters (linear regression) is treated first, discussing the distinct cases of known and unknown experimental uncertainties, finding confidence intervals for the optimal parameters ...
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The method of least squares is the basic tool of developing and verifying models by fitting theoretical curves to data. Fitting functions that linearly depend on model parameters (linear regression) is treated first, discussing the distinct cases of known and unknown experimental uncertainties, finding confidence intervals for the optimal parameters ...
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