太阳辐射真随机数发生器的拟合优度和随机性检验

S. G. Tanyer, K. D. Atalay, S. Ç. Inam
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引用次数: 4

摘要

随机数生成器(rng)是统计分析和优化方法(如蒙特卡罗、粒子群优化(PSO)和遗传算法)所必需的关键工具之一。现在有各种各样的伪rng和真rng,它们提供了足够的随机性。不幸的是,它们生成的数据并不总是准确地表示所需的分布,特别是当数据长度很小时。这可能会威胁到学术研究的“可重复性”。本文提出了一种基于均匀采样方法的真RNG (TRNG)算法。在这项工作中,太阳的射频发射mu - trng与众所周知的伪rng和真rng进行了比较测试。可以观察到,随机性和非常高的拟合优度都是可能的。
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Goodness-of-Fit and Randomness Tests for the Sun's Emissions True Random Number Generator
Random number generators (RNGs) are one of the key tools necessary for statistical analysis and optimization methods such as Monte Carlo, particle swarm optimization (PSO) and the genetic algorithm. Various pseudo and true RNGs are available today, and they provide sufficient randomness. Unfortunately, they generate data that do not always represent the required distribution accurately, especially when the data length is small. This could possibly threaten the 'repeatability' of an academic study. A novel true RNG (TRNG) using the method of uniform sampling (MUS) is recently proposed. In this work, the Sun's RF emissions MUS-TRNG is comparatively tested with well known pseudo and true RNGs. It is observed that both randomness and very high goodness-of-fit qualities are possible.
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