Sentence examples for modeling the uncertain from inspiring English sources

A new way of modeling the uncertain wind power injections was presented.

Based on interval mathematics, modeling the uncertain parameters as interval numbers, a non-probabilistic interval analysis method, which estimates the range of the nonlinear dynamic response with the help of Taylor series expansion, is presented, where the partial derivatives of the dynamic response with respect to uncertain parameters are considered to be interval numbers.

The fuzzy logic systems are used to model the uncertain nonlinear systems.

A modified approach for modelling the uncertain aeroelastic response near the boundaries of different regimes of behaviour is also presented.

We model the uncertain demand as a hybrid variable which describes the quantities with fuzziness and randomness.

While Monte Carlo simulation methods may be used to model the uncertain performance of such system, computational efficiency is a great challenge.

To model the uncertain factors, a normal distribution [16] is applied to describe the active and reactive load in the power system and the gas load in the natural gas system.

Assume a finite set of scenarios (varOmega =) {1, 2,…,s} to model the uncertain parameters; with each scenario (s in varOmega), we associate the subset {(d_{s}); (B_{s}); (C_{s}); (e_{s})} and the probability of the scenario (p_{s} (sumnolimits_{s = 1}^{s} {p_{s} = 1} )).

Assuming second order random fields to be appropriate for modelling the uncertain variations along the spatial extent in the macro-scale, statistical tests have been carried out to examine the suitability of some commonly used models for the probability density function for the various mechanical properties of the material.

In this paper, the imprecise probability uncertainties are treated with an interval random model, in which the probability variables are used to model the uncertain parameters and some distribution parameters of probability variables are expressed as interval variables instead of a precise value.

To model the uncertain perception of the environment, the true stimulus St was then corrupted by fast random noise to give the perceived stimulus, Sp, so that at any moment there was a given probability that if St was 1, say, Sp was 0 or −1.