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First, the concept of the standard deviation of the output signal (SDOS) of the ADC is proposed.
In other words, MSD assesses the maximal spectral deviation of the output signal with respect to the input signal over all channels and all frequencies.
A formula that estimates the standard deviation of the output differences of fixed-point and floating-point networks was developed in [8].
We consider a data generating process (DGP) that models the deviation of the output from the best practice frontier as the sum of two components, a one-sided inefficiency term and a two-sided random noise term.
First, a very useful indication about the overall performance of the neural network in explaining the damage increment results is provided by the standard deviation of the output residuals (RMSE) and the amount of reduction with respect to the raw set of damage increment values.
This error, which comes from postponing the triggering of actions, corresponds to the deviation of the output that should have been modified having the dataflow been completely executed; i.e., this error is calculated by summing the differences (in absolute value) between current and previous row's values and dividing by the sum of all previous values.
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Following the construction phase of the networks, the output was reproduced (network output) and the network was trained on input and output data until the deviation of the network output from the actual output was very small.
The above-mentioned errors are the deviations of the output of clustering algorithms from the ground truth.
Table 1 presents the means and standard deviations of the output variables and the normalized root-mean-square error (RMSE divided by the mean) of the in-sample dynamic forecasts for estimated output produced by the model.
This shows that the mean estimated parameter b is proportional to HSIC scaled by the ratio of the standard deviations of the output (phenotype ψ y)) and the input (SNP SNP interaction ϕ(x)= x A x B ).
This instantaneous nonlinearity is usually characterized by the AM/AM and AM/PM responses of the PA, where the output signal amplitude and phase deviation of the PA output are given as functions of the amplitude of its current input.
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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