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In the proposed method, the response of a HUSAS is approximated as a linear function of random variables using the stochastic perturbation analysis.
Next, the most influential parameters are randomized according to the Gaussian distribution and are employed in the dual probabilistic scheme – by using the stochastic perturbation and, independently, semi-analytical techniques to determine up to the fourth order probabilistic characteristics of the effective tensor components.
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Probabilistic analysis presented here is carried out using mainly the stochastic perturbation technique provided by the common application of the traditional FEM commercial code ABAQUS and the symbolic computations package MAPLE.
After the network and perturbation data were generated, the expression data was generated using GeneNetWeaver, using the stochastic differential equation model and otherwise using the default settings.
Endpoint impedance was calculated from the endpoint position and force data collected during the stochastic perturbation experiments using nonparametric system identification techniques we have described previously [23].
In addition, like in [1, 3 5], the stochastic perturbation in [7] was described using a standard Brownian motion instead of a fractional Brownian motion.
(b) Let us briefly consider a slightly different form of the stochastic perturbation.
In the present study, the stochastic perturbation is assumed to be an additive white noise process and equations for the evolving moments are derived using Ito differential rule.
Besides the parameters with the stochastic perturbation, there are other types of stochastic perturbations; see [13 19].
Sometimes, the stochastic perturbation of environment can make species extinct.
Finally, the influence of the stochastic perturbation is demonstrated by simulation.
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