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The proposed control methodology utilizes a backstepping procedure integrated with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters.
Multiple estimation models are used to tune the unknown parameters at the first level.
Hierarchical estimation approaches, usually a combination of multiple estimation models, have been proposed for solving some specific domain problems.
Using multiple estimation procedures, we found that all participants with a CPS record fared worse in adulthood than their peers without a CPS record.
In the paper by Goto [3], a multiple estimation method based on a MAP approach to detect melody and bass lines is described.
In the contribution by Klapuri [4, 5] a multiple estimation method based on the iterative estimation of harmonic amplitudes and cancellation is presented.
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They adopted a detailed econometric procedure involving multiple estimations using probit, instrumenting regressions and Tobit models.
From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation.
The processing speed achieved is higher than conventional camera frame rates, thus allowing the system to extract multiple estimations and be used as a platform to evaluate integration schemes of a population of neurons without increasing hardware resource demands.
In most cases, multiple estimations were carried out and means and SE of each determination have been reported.
Many localization methods for near-field cases have been proposed in the literature, such as maximum likelihood (ML), covariance approximation, multiple signal estimation (MUSIC), and estimation of signal parameters via rotational invariance techniques (ESPRIT) [16].
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