Central Limit Theorem based Cellular Automata for Generating Normal Random Numbers

Research Article
Central Limit Theorem based Cellular Automata for Generating Normal Random Numbers

Alireza Jaberi, Ramin Ayanzadeh, Elaheh Raisi and Aidin Sadighi

Information Technology Journal, 2013, 12(12), 2440-2446.


Taking into consideration the significance of normal distribution in statistical applications, it is fundamental to have an efficient and reliable method to generate Gaussian random numbers. This study proposes an innovative method to generate normal random numbers. The main novelties of this algorithm lie in the following: using multiple layers of cellular automata in which central limit theorem is applied to generate normal random numbers; and employing binary cellular automata motivated by Pseudo-Neumann neighborhood structure. To evaluate the functionality of proposed approach, extensive experiments have been carried out in terms of normality of generated random numbers. Simulation results show that multi-layer cellular automata produce better normal random numbers than MATLAB’s random number generator which justifies its efficiency and good performance in statistical tests.

ASCI-ID: 28-2203

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