By virtue of their approximation capabilities for nonlinear mappings, the feed-forward neural networks (FNN) have become ideal classifiers in many applications.
By virtue of the laminated approximation method, the state equations are then transformed into the ones with constant variables in each layer, and the state variable solutions are presented.
Nevertheless, by virtue of the normal prior distribution, normal approximation of the posterior function is useful.
In this paper, we introduce and analyze a relaxed iterative algorithm by virtue of Korpelevich's extragradient method, the viscosity approximation method, the hybrid steepest-descent method, the regularization method, and the averaged mapping approach to the gradient-projection algorithm.
The reverse of approximation is separation.
What this figure actually describes is a range of approximation.
In this paper, we develop the adaptive data fitting algorithms by virtue of the local property of the Progressive-iterative approximation (abbr. PIA), which generates the fitting curve (patch) by adjusting the control points of a blending curve (patch) iteratively.
Furthermore, a practical adaptive neural control algorithm is proposed by virtue of the dynamic surface control (DSC) technique, neural networks approximation and polar coordinates scheme.
The paper deals with the singular Volterra integral equation of the second kind, to which by virtue of 'the incompressibility' of the kernel the classical method of successive approximations is not applicable.
By virtue of multiparametric perturbation techniques, sensitivity analyses are performed to achieve an analytical asymptotic approximation of the dispersion functions.
