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Volterra type integral equations are appeared in many engineering fields, so that, we select Volterra integral equation of the first kind and wavelets as basis functions to estimate a solution for this kind of equations.
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In this aspect, we propose an artificial neural network (ANN) using a radial basis function to estimate and compensate the nonlinearity of the ITVCC.
To be able to estimate all shapes possible, we aim to increase the number of basis functions to include different types.
In this study, the polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate the optimal lens system parameters.
The LiDCO uses a simple power transfer function to estimate SV on a beat-to-beat basis and calculates CO from the mean SV values.
The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state.
The coefficients associated with basis functions are estimated using a "sparsity-promoting" regression approach which is an ensemble of two techniques: least squares and ℓ1-norm regularization.
The Slepian coefficients c k in Eq. (1) for these basis functions are estimated by least squares from the spherical cap observations.
At the training stage (see Section 2.3), the basis functions are estimated using the RWC musical instrument sound database [48, 49] and the full pitch range for each instrument.
In this study, we use a Bernstein polynomial basis [15] to estimate the arbitrage-free option price function.
"You certainly won't have a good basis to estimate revenue.
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