scholarly journals ERRORS IN MONTE CARLO SIMULATIONS USING SHIFT REGISTER RANDOM NUMBER GENERATORS

1996 ◽  
Vol 06 (06) ◽  
pp. 781-787 ◽  
Author(s):  
F. SCHMID ◽  
N. B. WILDING
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generator ◽  
Shift Register ◽  
Magnetization Distribution ◽  
Number Generator ◽  
Random Number Generators ◽  
Single Spin ◽  
System Volume

We report large systematic errors in Monte Carlo simulations of the tricritical Blume–Capel model using single spin Metropolis updating. The error, manifest as a 20% asymmetry in the magnetization distribution, is traced to the interplay between strong triplet correlations in the shift register random number generator and the large tricritical clusters. The effect of these correlations is visible only when the system volume is a multiple of the random number generator lag parameter. No such effects are observed in related models.

scholarly journals Difusi Bebas 1D dan 2D dengan Monte Carlo: Perbandingan Distribusi Bilangan Random Normal dan Seragam dengan Box-Müller

2021 ◽  
Vol 9 (1) ◽  
pp. 55-64
Author(s):  
Fairusy Fitria Haryani ◽  
◽  
Freddy Haryanto ◽  
Sparisoma Viridi ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Particle Distribution ◽  
Random Number Generator ◽  
Particle Interaction ◽  
Number Generator ◽  
Random Number Generators ◽  
High Concentrations ◽  
The Impact ◽  
Mc Method

Many biological processes in the human body are based on the diffusion system. Diffusion is defined as a process of random movement of the particle whose the direction is from high concentrations to low concentrations. Many of various study of diffusion have been done both experimentally and computationally. Because the particle interaction is stochastic, the Monte Carlo (MC) method is used in performing particle simulations. The main of MC method is the use of random numbers. Many software have provided uniform random number generators. But based on the analytic results, the solution is normal distribution. Therefore, Box-Müller can be used as a transformation of particle distribution. The software used, MATLAB, has a normal random generator. Therefore, the aims of this study is comparing particle distribution of these two different random number generator with MATLAB and showing the impact of timestep parameter to these random number generator. This result can be used as based for the modelling of more complex biological systems.

scholarly journals LINEAR FEEDBACK SHIFT REGISTER BASED UNIQUE RANDOM NUMBER GENERATOR

2014 ◽  
pp. 274-279
Author(s):  
HARSH KUMAR VERMA ◽  
RAVINDRA KUMAR SINGH
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Point Of View ◽  
Shift Register ◽  
Linear Feedback Shift Register ◽  
Linear Feedback ◽  
Number Generator ◽  
Random Number Generators ◽  
Random Sequences ◽  
Feedback Shift Register

Linear Feedback Shift Register based Unique Random Number Generator is an enhancement of Random Number generator with the additional property that any number generated by a unique random number generator can’t be duplicated. As per users demand for not duplicated random numbers in some applications like transferring a random number over the network on the behalf of actual character of the message for security point of view, existence of unique random number generators are very essential. In this paper LFSR [1] (Linear Feedback Shift Register) is used to implement the proposed concept of unique random number generator. Using LFSR is a faster approach for generating random sequences because it requires only X-OR operations and shift registers that’s why its implementation is very easy in software as well as in hardware [2, 3]. We can easily modify the LFSR and produce different random sequences. So it is the best option for using LFSR in unique random number generator.

Improving Random Number Generators in the Monte Carlo simulations via twisting and combining

2008 ◽  
Vol 178 (6) ◽  
pp. 401-408 ◽  
Author(s):  
Lih-Yuan Deng ◽  
Rui Guo ◽  
Dennis K.J. Lin ◽  
Fengshan Bai
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generators

SRAM Based Random Number Generator for Non-Repeating Pattern Generation

2014 ◽  
Vol 573 ◽  
pp. 181-186 ◽  
Author(s):  
G.P. Ramesh ◽  
A. Rajan
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Pattern Generation ◽  
Storage Device ◽  
Number Generator ◽  
Random Number Generators ◽  
Field Programmable ◽  
The Look

—Field-programmable gate array (FPGA) optimized random number generators (RNGs) are more resource-efficient than software-optimized RNGs because they can take advantage of bitwise operations and FPGA-specific features. A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. The many applications of randomness have led to the development of several different methods for generating random data. Several computational methods for random number generation exist, but often fall short of the goal of true randomness though they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible).LUT-SR Family of Uniform Random Number Generators are able to handle randomness only based on seeds that is loaded in the look up table. To make random generation efficient, we propose new approach based on SRAM storage device.Keywords: RNG, LFSR, SRAM

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