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Losonczy, A., Makara, J.K. & Magee, J.C. Compartmentalized dendritic plasticity and input feature storage in neurons.
This example uses the stack input feature to create three parameter files from three input files.
Losonczy, A., Makara, J. K. & Magee, J. C. Compartmentalized dendritic plasticity and input feature storage in neurons.
Results show that after adding RELBP_CI to input feature set cloud detection accuracy improves.
Firstly, the diagonal spectrum is calculated from vibration rotating machine as the input feature vector.
The input feature clusters were processed using parallel and cascade architecture with multiple input layers.
A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers.
A novel leak location method based on characteristic entropy is proposed to extract the input feature vectors.
An eigenvector based clustering method is employed to calculate the RBF centers in the input feature space.
Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.
This reduction in input feature volume led to a 16x reduction in the size of the DNN architecture and a 300x reduction in training time.
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CEO of Professional Science Editing for Scientists @ prosciediting.com