A Pseudo-Random Generator Whose Output is a Normal Sequence

2021 ◽  
pp. 1-9
Author(s):  
Boris Ryabko
Keyword(s):  
Computer Simulation ◽  
Random Number ◽  
Random Number Generators ◽  
Normal Sequence ◽  
Random Generator ◽  
Pseudo Random Number ◽  
Binary Word

Pseudo-random number generators (PRNGs) are widely used in computer simulation, cryptography, and many other fields. In this paper, we describe a PRNG class, which, firstly, has been successfully tested using the most powerful modern test batteries, and secondly, is proved to consist of generators that generate normal sequences. The latter property means that, for any generated sequence [Formula: see text] and any binary word [Formula: see text], we have [Formula: see text] where [Formula: see text] is the number of occurrences of [Formula: see text] in the sequence [Formula: see text], [Formula: see text].

Hardware implementation of pseudo-random number generators based on chaotic maps

2017 ◽  
Vol 90 (3) ◽  
pp. 1661-1670 ◽  
Author(s):  
Luis Gerardo de la Fraga ◽  
Esteban Torres-Pérez ◽  
Esteban Tlelo-Cuautle ◽  
Cuauhtemoc Mancillas-López
Keyword(s):  
Hardware Implementation ◽  
Random Number ◽  
Chaotic Maps ◽  
Random Number Generators ◽  
Pseudo Random Number

scholarly journals Problem of uniqueness in the renewal process generated by the uniform distribution

1992 ◽  
Vol 5 (4) ◽  
pp. 291-305 ◽  
Author(s):  
D. Ugrin-Šparac
Keyword(s):  
Integral Representation ◽  
Uniform Distribution ◽  
Gamma Function ◽  
Renewal Process ◽  
Random Number ◽  
Discrete Distribution ◽  
Inverse Image ◽  
Random Number Generators ◽  
Pseudo Random Number

The renewal process generated by the uniform distribution, when interpreted as a transformation of the uniform distribution into a discrete distribution, gives rise to the question of uniqueness of the inverse image. The paper deals with a particular problem from the described domain, that arose in the construction of a complex stochastic test intended to evaluate pseudo-random number generators. The connection of the treated problem with the question of a unique integral representation of Gamma-function is also mentioned.

scholarly journals Influence of Experiment Design in GPA Investigating with Respect to PRNGs

MENDEL ◽  
2018 ◽  
Vol 24 (2) ◽  
Author(s):  
Tomas Brandejsky
Keyword(s):  
Genetic Programming ◽  
Random Number ◽  
Experiment Design ◽  
Parallel Execution ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Programming Algorithms

This paper analyses the influence of experiment parameters onto the reliability of experiments with genetic programming algorithms. The paper is focused on the required number of experiments and especially on the influence of parallel execution which affect not only the order of thread execution but also behaviors of pseudo random number generators, which frequently do not respect recommendation of C++11 standard and are not implemented as thread safe. The observations and the effect of the suggested improvements are demonstrated on results of 720,000 experiments.

A General-Purpose Uniform Random Number Package

2021 ◽  
pp. 164-176
Author(s):  
E. Jack Chen
Keyword(s):  
Computer Simulation ◽  
Random Number ◽  
General Purpose ◽  
Random Numbers ◽  
Pseudorandom Generator ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Modeling And Analysis ◽  
Generating Sequences ◽  
Simulation Systems

As computer capacities and simulation technologies advance, simulation has become the method of choice for modeling and analysis. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Simulation studies are typically preceded by transforming in a more or less complicated way of a sequence of numbers between 0 and 1 produced by a pseudorandom generator into an observation of the measure of interest. Random numbers are a fundamental resource in science and technology. A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, random number generators also play an important role in cryptography and in the blockchain ecosystem. All samples of the sequence are generated independently of each other, and the value of the next sample in the sequence cannot be predicted, regardless of how many samples have already been produced.

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