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Exact(16)
We demonstrate the speed and accuracy of our method by using simulated data.
The validation of the algorithm performance is carried out by using simulated data generated with the GAMOS Monte Carlo.
The proposed system is fully developed and tested by using simulated data and live data in production.
By using simulated data to test the algorithms we have actual ground truth evaluate the performance of the method.
The approach is illustrated by using simulated data from idealized models of a stretched string and a bending beam.
The validity of the approach is demonstrated by using simulated data for two multi-degree of freedom systems.
Similar(44)
Our claims are supported by tests conducted using simulated data and TPC-H database benchmarking tools.
We have compared our GPNN to a traditional feed forward NN trained by back propagation using simulated data.
After each iteration step the unknown parameters of the resulting new model are determined by maximum likelihood estimation using simulated data.
Afterwards, we validate the WD approach using simulated data by generating large-scale instances of our advanced electricity CRA problem.
Convexity of the likelihood surface was also checked using simulated data, by plotting in the neighbourhood of the analytically determined maximum.
More suggestions(14)
by fitting simulated data
by using simulated images
by using simulated models
by using simulated scenarios
by using published data
by using simulated samples
by using simulated subtomograms
by using simulated replicates
by generating simulated data
by processing simulated data
by using simulated alignments
by reconstructing simulated data
by comparing simulated data
by using artificial data
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