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By using training data and feed forward structure for the neural network and also using particle swarm optimization for training the neural network based models, an optimum models was developed.
Different from the trivial resampling scheme for SSML channel models, an optimum resampling method is proposed in[14] for MSML channels, which aims at minimizing the average error of approximating the MSML channel by an SSML model.
Similarly, in discrete models, an optimum policy selects states with the highest value [4], [6].
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Furthermore, for this model, an optimum combination of foundation parameters exists to yield the maximum flutter load.
Based on this model, an optimum probe design can be obtained by balancing ultimate resolution with the signal-to-noise ratio by adjusting spacing and resistor values.
By integrating the verified STC simulation model with an optimum solver, a frictional losses reduced problem of bearing components has been investigated.
Optimization of the economic performance index of the process through the respective response surface models defines an optimum operation.
The model predicts an optimum thickness for porous mixed-conducting layers to improve oxygen surface exchange.
A mathematical model indicated an optimum point in which ethanol was the only organic solvent of the mobile phase.
The model predicts an optimum total system efficiency of 0.35 for a dimensionless receiver length of 0.86.
Furthermore, a case study of step-by-step modeling for an optimum flow field design has been presented in-details.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com