Returns the sum of the values of the data set.
First, we determine the parametric values of the data points using PSO.
Then, we compute the knot vector based on the parametric values of the data points.
The PMU's work on the phasor values of the data collected using Phasor estimation technique.
The 5th and 95th percentile values of the data were calculated.
This function returns the grouping and quality arrays for a set of groups defined by the low (binLowArray) and high (binHighArray) boundaries when applied to the axis values of the data (axisArray).
With assumptions to the statistical distribution of the training data, it allows to estimate not only the mean value but also well directed deviating values of the data distribution.
Then, all columns of the data matrix share similar waveforms and a core physical phenomenon arises, i.e., these singular values of the data matrix demonstrates a sparse distribution pattern.
The second stage uses information available in the numerical values of the data to search for high-level dimensionless variables/parameters, generated from simple combinations of dimensionless quantities generated in the first stage and which result in concise neural network models with improved performance characteristics.
It allows users to quickly develop statistical relations from data and then use those relations to forecast future values of the data.
The values of the data windows, chosen for approximating respectively, affect the resolution of the system.
