BRNNBi-Directional Recurrent Neural Networks
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Table 4 summarizes the results for training different BRNN of three layers.
Finally, the results of the proposed fault diagnosis model based on BRNN are compared with those of BPNN, RBF and GRNN, as shown in Table 5.
According to the comparison, the fault diagnosis model based on BRNN spends less time owing to the less hidden layer neurons while the model based on RBF or GRNN contains 56 hidden layer neurons.
After that, BRNN is introduced into the online fault diagnosis of BW, which is able to classify the fault data detected by the improved DBSCAN into different fault patterns.