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The target error probability is 10−7.
R e is the target error probability.
The errors of the neural networks were found to be quite small: 0.008 in the target error 0.01 and 0.007 in the target error 0.005.
The single target artificial neural networks (STANNs) perform best and predicts the global properties within a target error of 5.3%.
Note that the target error rates are transmitted to the decoder side in the header of the first frame and the target error rate is used as the conditional entropy of the LDPCA decoder.
The target error and the hidden layer number of the model can be obtained by the method of cross validation.
The ELM model needed many neurons in the hidden layer (41) to reach the set target error of 0.001.
In addition, the correlations between the estimated and measured values were found to be very high: 0.985 and 0.987 in the target error 0.05 and 0.005, respectively.
The level of accuracy of this approach is also dependant on the acceptance criteria and the target error level for admixtures.
The number of parity bits then determines a target error rate in one slice, which is decoded by an LDPCA in a single pass.
The neural network, trained by the 0.10 target error, presents a 0.086 error and a 0.850 correlation between the network outputs and the training sets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com