Abstract

Random plane generators may use various types of the random number algorithms to create multidimensional planes. At the same time, the discrete Descartes random planes have to be uniform. The matter is that using the concept of the uncontrolled random generation may lead to a result of weak quality due to initial sequences having either insufficient uniformity or skipping of the random numbers. This article offers a new approach for creating the absolute twisting uniform two-dimensional Descartes planes based on a model of complete twisting sequences of uniform random variables without repetitions or skipping. The simulation analyses confirm that the resulted random planes have an absolute uniformity. Moreover, combining the parameters of the original complete uniform sequences allows a significant increase in the number of created planes without using additional random access memory.

Highlights

  • In our previous studies (Deon and Menyaev, 2016a; 2016b; 2017) there were proposed several pseudorandom number generators, nsDeonYuliTwist32D, which offers a technique of using no congruential twisting array

  • This generator allows the creation of absolutely complete twister uniform sequences having various lengths

  • The direction of Random Plane (RP) Generators (RPG) employs a stochastic process at the time of creating the points distributed on N-dimensional plane

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Summary

Introduction

In our previous studies (Deon and Menyaev, 2016a; 2016b; 2017) there were proposed several pseudorandom number generators, nsDeonYuliTwist32D, which offers a technique of using no congruential twisting array. This generator allows the creation of absolutely complete twister uniform sequences having various lengths. The direction of Random Plane (RP) Generators (RPG) employs a stochastic process at the time of creating the points distributed on N-dimensional plane. We consider a two-Dimensional (2D) plane only. Other discrete-dimensional planes have the same initial properties. Each coordinate of RP-generated points may belong to its own Random Field (RF). An analysis of the last sources sums up the following selected types of random fields: Conditional RF (Quattoni et al, 2004; Sutton and McCallum, 2012), Markov RF

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