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By introducing a stochastic variable which satisfies Bernoulli distribution, the information of probabilistic time-varying delay is equivalently transformed into the deterministic time-varying delay with stochastic parameters.
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We can also modify model (1) by introducing a stochastic component with fixed volatility (sigma_{G}), representing on the one hand variable food intake, and on the other, variable glucose consumption due to activity, like exercise etc.
A new model is developed that can truly reflect this contradiction by introducing a stochastic process θ.
We first introduce a stochastic variable x defined by the PDF f(x) = ϑe ϑx θ( − x) expressing vehicle C's coordinate.
In the DSSN model, the time-varying delay is characterized by introducing a Bernoulli stochastic variable.
In the model, stochastic stress process is interpreted by the random distribution of stress amplitude and cycles at given amplitude, and randomness of fatigue resistance of material is described by introducing a random variable of single cycle fatigue damage.
Before introducing a stochastic model, we apply scale time and population by using non-dimensional variables t = c b t ˜ and defining x = b a u and by defining k = a b c, we obtain d x ( t ) d t = k x ( t ) ( 1 − x ( t ) − ∫ 0 t x ( s ) d s ), x ( 0 ) = x 0. (1).
By introducing the stochastic variables, a new model structure is built obeying Bernoulli distribution to describe the system and the controller.
The phenomenon of missing data is modeled by a stochastic variable following the Bernoulli random distribution.
The randomness of actuators failures is modeled by a stochastic variable in a Markov jump model framework.
We formulate a stochastic SIS epidemic model with vaccination by introducing a Lévy noise and regime switching into the epidemic model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com