On the Evaluation Criteria for Random Number Generators Using Monte Carlo Integration Algorithm


Among all the manifestations of chaos in numerous scientific domains, the use of chaotic systems to derive Random Number Generators is a hot topic. Although the core premise of chaos theory asserts that the ensuing series is deterministic, due to the tremendous sensitivity and dependency on the system’s initial conditions, one could approach the topic of randomness within the realms of chaos theory. In this paper, we describe eight mathematical strategies for applying to experimental data gathered from capacitor voltages in Chua’s circuit’s traditional design. Each of the eight proposed approaches is implemented as a function that generates randomly distributed values and is then compared to commercially available timer-based random generators. The Monte Carlo Integration Process, which serves as the research’s evaluator method, displays spectrum distributed data before grading the schemes using a visualisation of the alleged algorithm. Because the geometrical domain in Monte Carlo Integration is defined in such a way that the most randomly scattered data set results in a closer estimation of the number Pi, the suggested scheme, Frequency indicator, is ranked as the highest-ranked scheme in that regard, with a numerical value of 3.1424 for Pi.

Author (S) Details

Kasra Amini

Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany and Institute of Aerospace Thermodynamics (ITLR), University of Stuttgart, Stuttgart, Germany.

Aidin Momtaz

Department of Physics, Isfahan University of Technology, Isfahan, Iran.

Ehsan Qoreishi

Department of Physics, Isfahan University of Technology, Isfahan, Iran.

Sarah Amini
Department of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Sanaz Haddadian
System and Circuit Technology Group, Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany.

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