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The control system is converted into an averaged control system for energy processes by using the stochastic averaging method.
The approximate stationary solutions of the system are also obtained by using the stochastic averaging methods for quasi-Hamiltonian systems.
First, the partially averaged Itô stochastic differential equations are derived from given system by using the stochastic averaging method for quasi-integrable Hamiltonian systems.
First, the partially averaged Itô equation for the system amplitude is derived by using the stochastic averaging method for strongly non-linear systems.
It is observed that the numerical results by using the stochastic averaging method is in good agreement with that from digital simulation.
By means of stochastic transformations of state norm process, the stability boundaries are determined using the stochastic averaging method and a technique due to Khasminskii.
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We use the stochastic averaging theory to prove the local exponential convergence, both almost surely and in probability, to a small neighborhood near the source for elliptical level sets.
Observing results from a 1000 simulations using the stochastic model, the average prevalence of infection is progressively reduced with each periodic treatment as in the deterministic model.
Results show that using the stochastic information on return transports leads to average improvements of around 15%.
Then, the dynamical programming equation for non-linear stochastic optimal control of the system is derived from the averaged Itô equations by using the stochastic dynamical programming principle and solved to yield the optimal control law.
A comparison of testing strategies using the stochastic model.
More suggestions(15)
using the stochastic queue
using the stochastic cloud
using the stochastic finite-fault
using the stochastic framework
using the stochastic geometry
using the classical averaging
using the stochastic point
using the stochastic frontier
using the stochastic inversion
using the coherent averaging
using the stochastic stability
using the stochastic approach
using the automatic averaging
using the standard averaging
using the probabilistic averaging
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