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For modeling a real earthquake, the source model and wave propagation outside the target area should be examined.
While modeling a real nervous system, stochastic noises and parameter uncertainties are inevitable and should be taken into account.
When modeling a real process the effects of nonlinearities must be taken into account, especially when designing a control algorithm.
However, modeling a real structure to a robust quantitative (mathematical) representation is a very difficult, complex and computationally demanding task.
Since modeling a real antenna with precise values for main beam and side lobes/back lobes is difficult, we employ a simplistic antenna model [3,8,9] in this paper.
In this paper, we present a comparative study using ANNs and co-active neuro-fuzzy inference system (CANFIS) in modeling a real, complicated multi-input multi-output (multi-input multi-outputure process of roller kiln used in ceramulti-input multi-outputine.
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In activity 4, modeling a real-world system introduced new challenges related to choosing correct initial conditions.
The collaborative image retrieval scenario aims at modeling a real-world situation in which various users submit simultaneous queries to a retrieval system and provide their relevance feedback.
The presented PN based method is illustrated by modeling a real-time scheduling and control for flexible automotive manufacturing system (FAMS) in Valeo Turkey.
Since modeling a real-world system might be very time-consuming, a direct data-driven approach is employed to tune the controller parameters, without need of a mathematical description of the system.
The method is applied to model a real platform, namely, NXP LPC2106, and a theoretical multiprocessor platform.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com