Research Article Open Access

Twister Generator of Random Normal Numbers by Box-Muller Model

Aleksei F. Deon1 and Yulian A. Menyaev2
  • 1 N.E. Bauman Moscow State Technical University, Russia
  • 2 University of Arkansas for Medical Sciences, United States
Journal of Computer Science
Volume 16 No. 1, 2020, 1-13

DOI: https://doi.org/10.3844/jcssp.2020.1.13

Submitted On: 7 October 2019 Published On: 10 January 2020

How to Cite: Deon, A. F. & Menyaev, Y. A. (2020). Twister Generator of Random Normal Numbers by Box-Muller Model. Journal of Computer Science, 16(1), 1-13. https://doi.org/10.3844/jcssp.2020.1.13

Abstract

Twisting generators of the pseudorandom normal variables can use uniform random sequences as a basis. However, such technique could provide poor quality result in cases where the original sequences have insufficient uniformity or skipping of random values. This work offers a new approach for creating the random normal variables using the Box-Muller model as a basis together with the twisting generator of uniform planes. The simulation results confirm that the random variables obtained have a better approximation to normal Gaussian distribution. Moreover, combining this new approach with the tuning algorithm of basic twisting generation allows for a significantly increased the length of created sequences without using any additional random access memory of the computer.

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Keywords

  • Pseudorandom Number Generator
  • Stochastic Sequences
  • Congruential Numbers
  • Twister Generator
  • Normal Plane Generator