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Thermal indices are important for assessing human thermal perception and are widely used as indicators of heat strain as well as for designing, tourism and forecast purposes.
These parameters, or alternatively the propagation factor, M3000F2, and the critical frequency, foF2, of the F2 peak, which are related to hmF2 and NmF2, are used to anchor the electron density vertical profile computed with different models such as the International Reference Ionosphere (Bilitza, 2002), as well as for radio propagation forecast purposes.
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With this motivation, in this study we examine the performance of single layer feed-forward neural network (SLFN) in ISMR forecast and for the first time apply random vector functional link network (RVFL) and regularized online sequential network − RVFL (ROS-RVFL) for the forecast purpose and subsequently examine their relative performance with respect to SLFN.
A short-term hourly environmental temperature forecaster for using in building electric load forecasting purposes has been designed based on a state space Neural Network (ssNN).
The usefulness of input output analysis for forecasting purposes has been limited by a number of factors.
Overall the model performs very well, and the simulation results confirm that inclusion of prior information is of considerable value for forecasting purposes.
In general, a particular wavelet transform (and a particular set of levels of decomposition) is selected as the 'optimal' wavelet transform to be used for forecasting purposes.
The measurements of all stations are being processed automatically while converted into a suitable form, so as to be serviceably for forecasting purposes.
This method, called Automated ANNs, is an attempt to develop an automatic procedure for selecting the architecture of an artificial neural network for forecasting purposes.
Artificial neural networks (ANN) have been proved to be an effective alternative for forecasting purposes because of the pattern-matching ability.
However, because Mr. Weiner is no longer on the ballot, and because the magnitude of the incumbency advantage varies substantially from district to district, this is of marginal utility for forecasting purposes.
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