Schematic diagram of SAE. As shown in Figure 2A ... The development of the proposed framework consists of two phases: training the stacked autoencoder and construction of fuzzy information granules.

In addition, stacked TLapAE (STLapAE) is further constructed to extract deep feature representations of the data by hierarchically stacking TLapAE blocks. For model training, backward propagation ...

The first module introduces a stacked LSTM-nested deep-autoencoder, leveraging the synergies of LSTM cells and autoencoder. The second module utilizes the encoded latent features to construct a dense ...

To fill this research gap, in this article, a novel double-stacked autoencoder (DSAE) is proposed for a fast and accurate judgment of power transformer health conditions with an imbalanced data ...