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In the same scope, a different work gives some significant approximation formulas for derivatives of approximated functions through the application of univariate and multivariate quadratic spline quasi-interpolants.
By searching for the smallest difference between the exact probability values and those obtained from approximated functions with different q, the optimal values of q for two approximations were achieved which are 0.27 and 3.91 respectively in this particular case.
However, since NURBS functions neither reproduce any polynomial nor interpolate the grid points (also referred to as control or nodal points), the approximated functions result in uncontrolled errors over the domain including the grid points.
We also include the transfer function resulting when the singular term is removed, to which the approximated functions converge.
A robust nonlinear controller is then designed based on the approximated functions to satisfy the control objective under the sliding mode technique.
We then transform the algorithm with the approximated functions to fixed-point and provide a BER performance for the algorithm with the variables set to 16-bit word lengths using the hybrid "ScaledDouble" data types.
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Different types of wavelets and approximating functions have been used in numerical approximations.
Multivariate calculus: functions of two or three variables, approximating functions, partial differentiation, directional derivatives, multiple integration.
The wind speed distribution with height is analyzed, and the coefficients of approximating functions calculated.
Unlike the conventional fully connected feedforward multilayer neural networks for approximating functions on continuous input spaces, this paper investigates simplified neural networks (which use a common linear function in the hidden layer) for approximating functions on discrete input spaces.
This type of approximating functions has a faster rate of convergence as compared to the polynomial co-ordinate functions.
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