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The standard error measure (SEM) reflects the degree to which a measurement can vary as a result of error in the measurement process [ 26].
However, the statistical median PE measurement was found to be within the 5% objective error measure (1.3% for MAP, −3.5% for CO, and 3.95% for SVO2).
A convergence criterion and error measure are proposed.
Meeting 9: Edge Contraction Algorithm (PostScript), Meeting 10: Preserving Topology (PostScript), and Meeting 12: Error Measure (PostScript).
Instead of error gradient, Alopex uses local correlations between changes in individual weights and changes in the global error measure.
A global error measure for evaluation of the convergence is proposed.
The strain gage data and the computational results show a high level of correlation using the Russell error measure.
The DIC data and the computational results show a high level of correlation using the Russell error measure.
This convergence error measure is supported on the shock trajectories and, as we demonstrate here, is usually "small".
The algorithm does not make any assumptions about transfer functions of individual neurons, and does not explicitly depend on the functional form of the error measure.
The quality of the model is assessed using a mean square error measure that depends linearly on the parameters.
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CEO of Professional Science Editing for Scientists @ prosciediting.com