A small deviation in the initial conditions should cause a large modification in the output, that makes chaotic systems very attractive for

pseudo-random number generation.

The second defect here is that as the documentation states, the returned

pseudo-random number is "greater than or equal to mm Value and less than max Value", so this code actually produces a random number between one and five!

We generate the following

pseudo-random numbers and transmission factors:

where the rotation matrix [C.sub.j] was calculated as in (6), h is a standard deviation of angular noise, and [[[xi].sub.j], [[omega].sub.j], [[eta].sub.j] are

pseudo-random numbers obtained by the standard Gaussian generator (with zero mean and standard deviation equal to one).

--the generated sequence of

pseudo-random numbers is by the large period (up to [2.sup.255]), due to the "tap" sequence from the linear feedback shift register that has the structure of an irreducible polynomial in [Z.sub.2].

Therefore, it is necessary that, when testing a problem, one try a number of scenarios (i.e., sets of parameters such as age and spending amount) and of batches (i.e., repeating the same simulation over and over with different realizations of the

pseudo-random numbers).

So-called random number keys generated by computer are produced by an algorithm and are only

pseudo-random numbers. By altering your algorithm, an intruder might limit the number of keys generated to a nonrandom number series easily crackable by the intruder.

A sequence of five

pseudo-random numbers was generated for each of three different cases of SEED use.

The experimental quantities were replaced by a subset of the spindle unit design variables (as generated using a generator of

pseudo-random numbers with an equal probability distribution) and that of the results of calculations performed for the cases of matching the above variables (Wolny, 1992).

The experimental quantities were replaced by a set of the spindle assembly design variables (as generated using a generator of

pseudo-random numbers with an equal probability distribution) and that of the results of calculations performed for the cases of matching the above variables (Wolny, 1992).

The use of cebyse mixing to generate

pseudo-random numbers. J.

Key-dependent property involves using

pseudo-random numbers. Throughout this paper,

pseudo-random numbers are generated depending on a secret key, which is stored for the watermarking extractor.