Sentence examples for numerical training from inspiring English sources

The second group assumes no knowledge of any fuzzy rules and performs a cluster analysis on the numerical training data before formulating the rules from the computed clusters.

The multi-objective optimization was performed by means of modeFRONTIER® using meta-models generated over a selected set of experimental and numerical training designs.

While the computational structure of a system is manually crafted by human experts in the former case, self-organizing neural fuzzy systems that are able to automatically extract generalized knowledge from batches of numerical training data are proposed for the latter.

The authors administered a simple numerical training, finding improved ability in the numerical but not in the spatial task.

For example, stimulating the IPS in a subject with low numerical abilities during numerical training may not optimize cognitive training because the subject might apply different strategies than other individuals of the same age; the subject may therefore recruit frontal regions, instead, for the same type of processing (Rivera et al., 2005).

The short-term wind forecast system is based on a mixed statistical-numerical procedure, trained by means of local wind measurements and implemented into an operational chain for the real-time prediction of the maximum expected wind velocity corresponding to three forecast horizons (30, 60 and 90 min) and three non-exceeding probabilities (90%, 95%, and 99%).

In order to obtain sufficient data for the network training, numerical simulation was used to simulate different ranges of misfires in the engine.

The numerical example of train delays and route conflicts shows that the D-Agent can generally perform as a station dispatcher in fulfilling the specific tasks, estimate the traffic state in different operation strategies and support the decision-making of favored solutions.

A gradient steepest descent method to adjust the MRL filter parameters (learning process), using the Least Mean Squares (LMS) algorithm, and a systematic approach to overcome the problem of non-differentiability of the morphological-rank operator are used to improve the numerical robustness of the training algorithm.

While the finite element method is by far the most popular engineering design tool of the digital age, widespread use by the mining community has been impeded by the relatively high cost of and the need for lengthy specialized training in numerical methods.

Children completed adaptive training on numerical comparison for half an hour a day, four days a week over a period of five-weeks.