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The algorithm stops when the deviation between two iterations is ≤0.1%.

We thus use this as the stop criterion and terminate the estimation process when the difference of estimated input SNRs between two iterations is less than 0.5 dB.

At each time step, the simulation proceeds only if the solution converges, that is, if the difference in pore pressure between two iterations is less than the specified tolerance.

Convergence is reached if the percentage of signal changes between two iterations is less than the specified convergence threshold at all the nodes.

3) In order to achieve good convergence of the training, the maximum number of iterations of the neural network training parameters is set to 2000; the MSE difference threshold between two iterations is set at 0.005, and other parameters adopt the default settings.

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In the article, we use a threshold (0.1) and stop the iteration if the distance between adjacent two iterations is less than the threshold.

The algorithm converges to V ∗ when the maximum error between the last two iterations is less than a small constant ε, for all state s∈S.

Two iterations were sufficient to achieve tolerance of ∼0.1%.

Two iterations were needed for this data set.

Once prior values were chosen, the program was executed and several chains in between one and two million iterations were calculated, depending on the sign of the direct-maternal genetic covariances, the degrees of belief assigned to the parameters, and the number of samples discarded as burn-in.

Three iterations were completed between August and December 2012.

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