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An alternative to reduce the tracking error is reducing localization errors.
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Using conventional neuron based ANN compensation, the error is reduced to 1% error.
The motor-angle error is reduced substantially in all experiments and the tool-path error is reduced in most of the cases.
The averaged correction error is reduced by 17%, comparing with STORM.
The results show that with two and four times compensation calculations, the error is reduced to 10%and2%2% respectively.
Declination error is reduced from 7.074° to 0.331° (4.6% of the former value).
For a given NAPL architecture the error is reduced with each added population.
The results show that the mean value of synchronous triggering error is reduced to 87.85 ns.
The simulation result after compensation shows that the shrinkage error is reduced as 80.8%.
In the Southern Andes, the relative contribution of the geodetic error is reduced by the large sample size, while glaciological and interpolation errors feature large absolute values.
For next-day forecasts the prediction error is reduced from over 35% for traditional NWP models to 14-22%.
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