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It can simultaneously learn the relation pattern's information and the semantic properties of given entities.
It is employed to learn the relation between the parameter matrix and the individual core tensor.
In this case, using AI techniques to learn the relation between monitored parameters and system health is the best solution.
Basically, multi-criteria decision making problems are formulated, and neural network is used to learn the relation among criteria and alternatives and rank the alternatives.
The results have shown that the both ANN and GEP are found to be able to learn the relation between CBR and basic soil properties.
Within this framework, Artificial Neural Networks (ANN) seem to be promising in reproducing the material mechanical behaviour, since they can be trained to learn the relation between input (strain) and output (stress) without the need of setting any explicit analytic stress-strain law.
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In this paper, the feed-forward neural network with one hidden layer is used to learn the relations in DLCSV space.
The system developed here employs supervised mathematical learning and error retropropagation techniques to learn the relations between biophysical and clinically assessed parameters of subjects.
During the first five sessions with all relations presented in mixed order within sessions, the patient did not learn the relations and even was at 0% correct for the A2 → B2 trials in sessions 3 and 5. Therefore, the procedure was simplified to present only two of the relations within each session until the patient learned the relations.
Because the patient did not learn the relations in these five sessions, the procedure was simplified to two relations each session until the patient achieved at least 90% correct.
Hidden layers enrich the network for learning the relation between input and output data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com