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Then, the desired parameters are extracted from LUTs computed off-line.
In this section, we present the new joint estimator for the desired parameters.
Then, it extracts the desired parameters from LUTs computed off-line.
This is a valuable method to fine-tune properties to the desired parameters.
Adaptive parameter estimation has two primary objectives: estimation and tracking of the desired parameters.
After simulations, Taguchi method is used for finding the optimum condition for the desired parameters.
For this reason, prediction models are commonly used to obtain desired parameters indirectly.
In contrast to parametric methods, the Bayesian approach treats the desired parameters as random variable with a-priori known statistics.
The initial sensing rate is determined using historical data by setting the desired parameters in Eq. (2).
Closed-form expressions are developed for the desired parameters using the modules and the phases of the cross-correlation coefficients.
Modeling of complex core design, calculation of desired parameters, and visualization of the complete model were all performed using SuperMC.
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