Sentence examples for both the approximated function from inspiring English sources

The results show that convergence rate of the -functional depends upon the smoothness of both the approximated function and the reproducing kernels.

Corollary 2.2 shows that the convergence rate of the -functional (1.8) is controlled by the smoothness of both the reproducing kernels and the approximated function.

The function that a learning classifier with one output approximates to, when has been trained to minimize the Cross-Entropy error, is calculated in Section 3. The condition stated in[1] is applied to demonstrate that the approximated function is useful to approximate the NP detector.

Since the efficient procedure of selecting wavelets used in the OLS method is not very sensitive to the input dimension, the dimension of the approximated function does not cause the bottleneck for constructing FWN.

We pursue the approach described in (J. Fröhlich and K. Schneider,Europ. J. Mech. B/Fluids13,439, 1994) which is based on the explicit computation of the scalewise contributions of the approximated function to the values at points of hierarchical grids.

The error estimate of the approximated function was obtained by using the fuzzy Taylor theorem [34] for the function which is (i -gH-differentiable.

There are different ways of how to map with help of the look-up table an arbitrary input value of the approximated function to the corresponding function value.

The advantage of the neural network representation of the approximated function is greater flexibility of the basic functions and their ability to adapt (Haykin [1999]).

The original operating method was based on a new fourth-order approximation function, having a superior-order polynomial series that match the polynomial series of the approximated function.

For the MTM schemes, we use the following procedure: given the MTM outputs obtained in one run, we use these samples as nodes, then construct the approximated function using the construction P4 (considering these nodes), and finally compute the normalizing constant of this approximated function.

A robust nonlinear controller is then designed based on the approximated functions to satisfy the control objective under the sliding mode technique.