A Stochastic Convergence Analysis of Random Number Generators as applied to Error Propagation using Monte Carlo method and Unscented Transformation Technique

被引：0
|
作者：
Ram, Sangeetha Prasanna [1 ]
Nair, Jayalekshmi [2 ]
Ganesan, S. [3 ]
机构：
[1] VES Inst Technol, Instrumentat Dept, Bombay, Maharashtra, India
[2] VES Inst Technol, Bombay, Maharashtra, India
[3] BARC, Reactor Design & Dev Grp, Reactor Phys Design Div, Bombay, Maharashtra, India
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, INFORMATICS, COMMUNICATION AND ENERGY SYSTEMS (SPICES) | 2017年
关键词：
Monte Carlo method; Unscented transformation; Stochastic convergence; Random number generators; Nuclear data;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper compares the stochastic convergence of the Uniform Random number generators of two simulation software namely Matlab and Python and establishes the significance in choosing the right random number generator for error propagation studies. It further discusses about the application of Gaussian type of these random number generators to nonlinear cases of Error propagation using the Monte Carlo method and unscented transformation technique by means of a nonlinear transformation of one dimensional random variable of nuclear data.
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页数：7
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