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As a result, the neural network forecast model obtains higher accuracy, compared with common linear recession method which the mean error is 20.7%.
Once the model is calibrated, the tuned mean error is computed.
The results in Table 2 show that the mean error is negative in both samples.
At the R52 dataset the difference of mean error is significant in favor of confidence.
For this sensor, the mean error is 1.7%, and the maximum peak error is 11.9%.
For corresponding 3-D coordinates in the scene, the mean error is as small as 1.3 mm.
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The measurement mean error was less than 1.5% in velocity range of 0 30 m/s.
The mean error was 9.6 mm in three-dimensional (3D) verification.
The root mean squared error and residual mean error are 1.6 and −0.66 m respectively.
If λ is higher, the atoms that satisfy the required mean error are fewer.
The maximum and arithmetic mean error are shown in the last two columns.
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