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We examine the efficacy of our methodology in the presence of a step change and a spike in the magnetic field time series in Figs. 2 and 3, respectively.
The results show that the proposed ANN-based methodology outperforms the MLE in estimating the time of step change in the process variability with multivariate-attribute quality characteristics.
Theories change; methodology does not.
Third, pollsters should not change methodology in the last round of polling data without full disclosure.
A step change is needed.
Watch for the step change.
Step change.
Step change model.
This is a real step change".
We need a step change.
As the main contribution in this paper, we propose a modular methodology based on artificial neural networks for estimating the step changes in the covariance matrix of multivariate-attribute processes.
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