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The temporal evolution of the error cannot be reflected.
An expression is derived for the evolution of the error in a manifold approximation.
Figure 3 The evolution of the error norm of agent 1 under (pmb{alpha =0.3}), (pmb{mu=0.2}).
Thus, protocol (6) can solve the finite-time average consensus problem. Figure 3 shows the evolution of the error norm of agent 1 under protocol (6).
The evolution of the error rate and the number of known classes over text corpora is shown in Figs. 7a and 7b with curves for each selection strategy under evaluation.
However, as each of the three angles has a different effect on the evolution of the error on our simulated trajectory, it is easy to identify an error in one angle.
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Table 3 Mean errors (ME) vs correlation length (CL) (evolution of the mean error on the estimated radius values, respect to the noise correlation length) CL 14 22 30 36 ME ρ = 50 4.84 2.25 0.95 0.46 ρ = 43 5.63 2.32 1.29 0.78.
Figure 5 e) displays the evolution of the MGPS error e = ( e 1, e 2, e 3, e 4 ) T which tends to zero as t → ∞, which implies that the error system (3.14) between the drive and response systems (3.11 - 3.12) is globally and asymptotically stable.
The image (c) depicts the evolution of the restoration error.
Fig. 5 Evolution of the cumulative error distributions during training.
Table 1 presents the evolution of the Digit Error Rate (DER) according to the model size.
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