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Irregular systems are created by perturbing the position p of each seed by a stochastic vector δp.
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Currently, the standard approach is to model wind velocities as discrete components of a multivariate stochastic vector process, characterized by a Cross-Spectral Density Matrix.
where is continuously differentiable, is a -dimensional stochastic vector, and are given vectors, and are given stochastic matrices.
Under this model, the channel is treated as a stochastic random vector at the receiver.
This paper develops a hybrid approach of spectral representation and random function for simulating stationary stochastic vector processes.
The optimization model (23) developed in section 2 can be summarized by (36), where ({varvec{P}}_{{{text{AC}},d}}) and ({varvec{P}}_{{{text{AC}},fs}}) are the decision vector and fuzzy stochastic vector of the AC area, and ({varvec{P}}_{{{text{DC}},d}}) and ({varvec{P}}_{{{text{DC}},fs}}) are the decision vector and fuzzy stochastic vector of the DC area, respectively.
All the random variables in a stochastic system can be described by a stochastic process.
The elongation step is modeled by a stochastic method.
Daily travel distance is generated by a stochastic model.
Our experimental data were also supported by a stochastic approach.
The implementation has been complemented by a stochastic backtracing facility.
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