Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance

In three linesRGNet, a neural network architecture based on the renormalization group, addresses class imbalance and multidimensional noise for fault diagnosis. The model hierarchically compresses feature space and captures both local details and global patterns. Tested on imbalanced AI4I dataset.

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