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This study deals with the problem of consensus error calculation for linear multi-agent systems (MASs) with system noises and measurement noises.
b Error calculation for large-signal HB training.
a Error calculation for combined dc and small-signal S parameter training.
The training error calculation of the general Neuro-SM model for combined DC and S parameter training as well as HB training further illustrates in Fig. 2. Figure 2a, b is error calculation for combined dc and small-signal S parameter training as well as large-signal HB training, respectively.
For the composition model, the results of the error calculation for glucan, xylan, and lignin were averaged for a given sample and then compared to 1.5.
The error calculation for a measured value was obtained by multiplying the point estimate for the SEM by the z-value associated with the 90% confidence interval (z = 1.65).
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The error calculations for pool 3 5 all fell within the TE budget of Ricos-Fraser.
Fig. 2 Block diagram for error calculation of the general Neuro-SM model.
TA minimal gain of 0.1 to produce a split, a maximal tree depth of 20, a confidence level of 0.25 for the pessimistic error calculation of pruning and the number of alternative nodes of 3 when pre-pruning would prevent a split.
For models run with a minimal size of 4 for a node to allow a split, a minimal size of 2 for all leaves, a minimal gain of 0.1 to produce a split, and a maximal tree depth of 20, the confidence level of 0.25 for the pessimistic error calculation of pruning and the number of alternative nodes of 3 when prepruning would prevent a split.
The models were run with the minimal size of 4 for a node to allow a split, a minimal size of 2 for all leaves, a minimal gain of 0.1 to produce a split, a maximal tree depth of 20, and a confidence level of 0.25 for the pessimistic error calculation of pruning and the number of alternative nodes of 3 when prepruning would prevent a split.
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