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The expected values of the S-system parameters estimated at 90% confidence level are represented in Table 1 (gij) and Table 2 (αij, βij), which demonstrates the excellent performance of the algorithm in identifying network parameters even from noisy data.
The sample variance of the structural system parameter estimates decreases with the reciprocal of the duration of the overall non-stationary record.
Under reasonable conditions the theoretical variance of the structural system parameter estimates decreases with the duration of the long non-stationary record.
The output of these observers are estimates of the system parameters, estimates of the system dynamics in configuration, motion and acceleration level.
Procedural problems are discussed as well as results concerning LEO orbit and Earth system parameter estimates.
Standard errors (SE) and 95% confidence intervals (CI) for the mating system parameter estimates were obtained from 1000 bootstrap replicates of the data.
In the developed adaptive AQM (ACDM), the output feedback pole placement is implemented in an indirect model reference adaptive control (MRAC) scheme, in which the closed loop characteristic polynomial is determined by CDM and the system parameters are estimated using a modified recursive gradient method.
Next, the system parameters were estimated using a calibration method, which adjusted the parameters in the reconstruction algorithm, until the resolution and co-registration were optimized.
All mating system parameters were estimated at population level.
Mating system parameters were estimated at the population level using a maximum-likelihood approach under a mixed-mating system model (Ritland 2002).
Standard errors of the mating system parameters were estimated based on 500 bootstraps considering the maternal family array as the resampling unit.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com