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Multilayered back propagation feed forward neural network was used to predict the thermo-physiological properties of plated fabrics.
A module has been developed for real time computation and compensation of positioning error of an axis through a open architecture motion controller, using back propagation feed forward neural network.
Respiratory growth can be ensured through optimization of the propagation feed rate and the use of an exponential feeding profile that keeps the specific sugar addition rate lower than the rate that offsets overflow metabolism.
Higher amounts of hydrolysate in the propagation feed further improved the fermentation but increased the variability in fermentation outcomes and resulted in up to 20%% loss of cell mass yield.
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A back-propagation feed forward neural network was trained and used to predict the air losses from the Feldmoching tunnel.
A three-layer back-propagation feed-forward neural network is used as the classifier.
The artificial neural networks used are based on the principles of back-propagation feed-forward network.
Various NN structures were trained based on the back-propagation feed-forward approach.
There are different variations of the Artificial Neural Network (ANN), such as back-propagation, feed-forward, time delay, and error correction [5].
An innovative Structural Health Monitoring (SHM) methodology, based on structural strain measurements, which are processed by a back-propagation feed-forward Artificial Neural Network (ANN), is proposed.
However, decision-feedback receivers suffer from the major drawback of error propagation caused by feeding back erroneous decisions.
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