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The ANN is trained with a modified back-propagation algorithm.
An ANN is trained with simulated data based on the petrophysical model.
Swallows swoop in great arcs, and a sheepdog is trained with piercing whistles.
Each category is trained with 3 objects (600 examples per category).
A classifier is trained with case samples drawn from the patient population.
The BPNN is trained with the pressure data time series measured from adjacent pressure taps.
Each neural network cell is trained with critical status of a subsystem of the machine.
The network is trained with 97% accuracy and performance of the network in testing is 96%.
The developed ANN is trained with the extensive simulated results, and tested for typical cases.
In Stage 1, ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector.
As the network is trained with data, it will automatically store some information to a memory matrix.
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CEO of Professional Science Editing for Scientists @ prosciediting.com