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A neural network approach shows that ANNs can be trained to model fretting-fatigue phenomena.
Given sufficient data and complexity, ANNs can be trained to model any relationship between a series of independent and dependent variables.
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Based on the features extracted from normal bearing vibration signals, an HMM was trained to model the normal bearing operating condition.
They have been trained to model the empty world on the empty set.
In this approach, a global variance model is trained to model the variation of temporal parameter trajectories at an utterance level.
Local AAMs (LAAMs) are trained to model a specific facial area and decompose its variance from the rest of the facial variance.
The entire learning procedure for a DBM with L hidden layers is summarized in Algorithm 2. In layer-by-layer training procedure, the first RBM in the hierarchy is trained to model the given input observation.
A recurrent neural network (RNN) was trained to model a TRN that mimics the associated network motif.
Once the neural network was created, it was trained to accurately model the given phenomenon using the experimental data.
Many approaches are trained to produce models scoring better according to some evaluation method.
In addition, the educators of the Natural model were trained to lead the new model during a 2-day workshop.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com