According to [12] the model dependence of data transmission rate on signal power at the receiver input s is a jump function which increases with power growth.

Considering the attenuation model we describe dependence of throughput on the distance of jump function (see Figure 5):

The nonstandard jump functions used in the study are as follows: for standard jump function [psi](x) with [[psi]] = [[psi].sub.1] - [[psi].sub.0] at x = 0, and a nonstandard jump function is defined in the following form Salas and Iollo [1] and Baty et.

Along the characteristic curve, nonstandard jump function across the shock front can be written in terms of non dimensional variables [pi], g, v and [xi] in the following form

If f is continuous at [theta], then the jump function [f]([theta]) = 0.

which is constructed to approximate the jump function [f]([theta]).

where [lambda] = 1 in this Poisson law; k is an intensity parameter of the jump function; [PI] repeats the range of the jump function noticed on the history reports previously mentioned; and [theta] represents the points of the rate curve [1,30] years in EUR and USD.

(18.) DJ is a jump function which follows a Poisson law with an intensity parameter [lambda] and a jump range equal to [PI] > 0

This system of artificial intelligence works with the newly developed High Speed

Jump function to amazingly sharpen productivity.

By using a non-constant

JUMP function, we are able to move x forward by various distances without the need of additional searching to find where to move x.

If [rho] and [sigma] denote its associated

jump functions, then we denote by [??] and [??] the

jump functions associated to [T.sup.*].

The maximal error appears in T areas where steps used for [f.sup.F] (t) on T are significantly larger than the corresponding

jump functions on T.