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This chapter covers the basic design principles and methods for uniform random number generators used in simulation.
Thus, designing suitable and independent uniform random number generators for the sub-tasks has become a very important issue in large-scale parallel simulations.
The ensemble Kalman filter was then used to update the uniform random number realizations, which were then used to generate a new facies realization by multiple-point simulation.
Here, we explore the effect of augmenting two different chaotic maps along with the uniform random number generator (RNG) in the popular MOO algorithm the Non-dominated Sorting Genetic Algorithm-II (NSGA-II).
Two commonly used uniform random number generators, linear congruential generator (LCG) and multiple recursive generator (MRG), have served as backbone generators for some parallel random number generators constructed in the past.
To test the noise resilience of Fuzzy Controllers, the design of a Fuzzy Controller for a mobile robot is presented in this paper, in conjunction with three types of external perturbations: band-limited white noise, pulse noise, and uniform random number noise.
In our experiments, channel packet loss is simulated by using uniform random number generators.
Uniform random number block was coded to produce 32-bit uniformly distributed samples with periodicity 1010.
For this part one uniform random number is generated in the range of (0 1).
The weights are of the form (7). where, is a uniform random number.
A. Generate a uniform random number χ(t), χ(t)∈[0,1].
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