# GENERACION DE NUMEROS PSEUDOALEATORIOS PDF

No entraremos en detalle de cómo se obtuvo el valor de “C”, pero será establecido que el valor de. c= 10^(-p) (A ±B). La cual proveerá. Generacion de Numeros Aleatorios – Free download as Powerpoint Presentation .ppt /.pptx), PDF File .pdf), Text File .txt) or view presentation slides online. Generación de Números Pseudo Aleatorios. generacion-de-numeros- aleatorios. 41 views. Share; Like; Download.

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Improvement algorithm of random numbers generators used intensively on simulation of stochastic processes. ABSTRACT Choice of effective and efficient algorithms for generation of random numbers is a key problem in simulations of stochastic processes; diffusion among them. The random walk model and the Langevin’s dynamical equation are the simplest ways to study computationally the diffusion.

Both models, in the non-interacting free particles approximation, are used to test the quality of the random number generators which will be used in more complex computational simulations.

### Distribución normal de números aleatorios (artículo) | Khan Academy

In principle, generation of random numbers via computers is impossible because numeroa work through determinist algorithms; however, there are determinist generators which generate sequences of numbers that for practical applications could be considered random.

In the present paper we present a improve algorithm random number generator obtained from a combination of those reported by Numerical Recipes, GNU Scientific Library, and that used by Linux operating system based on hardware. The results obtained using our computational tool allows to improve the random characteristics of any pseudorandom generator, and the subsequent improving of the accuracy and efficiency of computational simulations of stochastic processes. Diffusion, random walk, langevin’s dynamical equation, random number generators, stochastic processes.

Random Number Generator RNG is a key point for the simulation of stochastic processes, particularly when the Monte Carlo method is used.

Diffusion is among most common phenomenona in nature; moreover it is suitable to be pseudoaleatorjos studied. Both models, in the non-interacting free particles gemeracion, are used to test the quality of the random number generators Janke, ; Passerat-Palmbach, In the first model, RNG is used to pseudoaaleatorios the molecular displacement by jumping; in the second one, to simulate the force on each particle, when the thermal noise is considered.

Generating random numbers by using computers is, in principle, unmanageable, because computers work with deterministic algorithms.

However, there are deterministic algorithms that produce sequences of random numbers which for practical proposes can be considered random; these algorithms are named pseudorandom. Rosin, Yates and Klingbeil, Large simulation processes need good accuracy of results and low run time consumption as criteria of RNG selection. In this paper, we study the behavior of the solutions in case of diffusion of free non interacting particles by using the RWM and LDE; to generate random numbers we use some of the most popular RNG, they are: Here, we propose a new algorithm to improve the random characteristic of any pseudorandom generator, and subsequently improving the accuracy and efficiency of computational simulations of stochastic processes.

The last should be undertaken as an independent sequence of random numbers whith the same probability of occurrence. Besides they have a long period and computational efficiency taking into account: The computational algorithms for generating a pseudorandom numbers can be classified as: Overall, all the PRNGs generate a sequence depending on starting value called seed and, consequently, whenever they are initialized with a same value the sequence is repeated.

The implementation of this PRNG is very simple follow a algorithms represented on a function GetUrand to obtain a uniform generator on [0;1] interval, that depends of the number N of random bits that was read.

## Distribución normal de números aleatorios

One of the major deficiencies that have the PRNG is its sequences are determined by the random seed, this may be a mechanism that can be used to improve the characteristics of the PRNG if after a set of calls, optimized in correspondence dw the computational architecture, the seed is restart using other PRNG of operating system, in each case by optimizing the pseudoaleatroios of iterations for which there is sufficient accumulated environmental noise, this method breaks the sequence of decreasing PRNG long-term correlation between the values of the sequence and increasing the random statistical properties.

The algorithms to use this mechanism of improvements that we propose can use any PRNG, represented as Rand function, and depend of the number M of iterations to do the reseed as show on function GetBetRand. Basic models for the simulation of stochastic processes. Diffusive processes are stochastic processes whose behavior can be simply simulated through the random walker model RW and Langevin dynamics equation DL.

In practice, a computer simulation model RW is to build a system S which particles move with displacements.

The DL model is a simplified approach to describe the dynamics of a molecular system, this takes into account the interaction of each molecule with the environment in which broadcasts which is treated as a viscous medium and includes a term corresponding to the thermal agitation in the case of particles that do not interact with each other, it has the form: When rms is calculating this gives: Importantly, the expressions 1 and 3 that are used to generate shifts in the RW model and noise in DL are highly influenced by the quality of the generator used, because the generation of random numbers corresponding to three consecutive calls are needed and implies that the sets of possible values generated can be limited by the correlations, the ability to generate 3 calls at least 2 components of equal value is almost null then all possible directions as, may not be generated.

In the case of the simulation model DL we used the following parameters: More details of other statistical tests for PRNGs can be consulted on the url: In the study of central limit average behavior the DL model was better and the study of the standard deviation of the theoretical value was more appropriate RW model for the proposed system. In all the cases we observe that the PRNG give better results when using PRNG seeding with the Linux kernel PRNG, this result is confirmed for all proposed PRNGs when the number of calls to reset is optimized such that time to gather enough operating system noise with the expression proposed, without affecting significantly the response speed of the PRNG, a factor which is principal for the development of long runs.

We only show illustratively only two of the most widely PRNGs used. One per software distribution. Computer Physics Communications, Numerical Methods for Ordinary Differential Systems. Investigations on the theory of the brownian movement. A random number generator based on unpredictable chaotic functions. Monte Carlo Concepts, Algorithms and Applications. Generation and quality checks.

From Theory to Algorithms, Lecture Notes, volume 10, p. A very fast shift-register sequence random number generator. Journal of Computational Physics, Operations Research, 44 5: Maximally Equidistributed Combined Tausworthe Generators.

Mathematics of Computation, 65 Mathematics of Computation, 68 A search for good multiple recursive random number generators. A search for good multiple recursive random number generators, 3: A portable high-quality random number generator for lattice field theory calculations. A dimensionally equidistributed uniform pseudorandom number generator.

Journal of cryptology, 5: Good ones pseudoaleatoriox hard to find. Communications of the ACM, 31 Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations. The art of scientific computing.

### GENERADOR DE NUMEROS PSEUDOALEATORIOS by jose antonio gomez ramirez on Prezi

University Press, c, Third Edition. How to improve a random number generator. Ultrafast physical generation of random numbers using hybrid boolean networks. Physical Review E, 87May A statistical test suite for random and pseudorandom number generators for cryptographic applications, Molecular Modeling and Simulation. Recycling random numbers in the stochastic simulation algorithm, January Four-tap shift-register-sequence random-number generators.

Computers in Physics, 12 4: