On the interface we present the approximation error
in the form
and the (2N + 1)-term approximation error
is given by
Our estimates improve the previous known results in several aspects: Firstly, the anisotropic approximation error
is obtained in a different way.
j]) basis functions and it is shown that under sufficient smoothness an approximation error
of order [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] can be achieved.
However, in both presentations one can only find fairly crude estimates for the related global approximation error
2 is the approximation error
and the boundary term involved there is specifically generated by the discretization of variational inequalities.
Based on the weighted-residual error estimator from , we introduced an overall error estimator which controls both, the discretization error as well as the data approximation error
J]) of symbols of the multiple Gabor multiplier that minimizes the approximation error
[[parallel]T - [G.
3, the smallest approximation error
was achieved for Learning rate: 0,01, Momentum: 0,4, Sigmoid's alpha value: 0,4, Neurons in first layer: 2.
It has been noticed that, the interrelation between the growth of an entire function in terms of [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and approximation error
Q] approximating this error, and random approximation error
The procedure is halted when the desired number of the linear segments M is reached, or the approximation error
is below given threshold [epsilon].