BRNNBranch Network Node
BRNNBi-Directional Recurrent Neural Networks
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In BRNN the weights are considered as random variables with Gaussian distribution and thus their density function can be updated as:
In this paper, a three layered feedforward BRNN including one input layer, one hidden layer, and one output layer is developed as a classifier to identify the given binary fault pattern of BW.
The improved DBSCAN and BRNN are realized with MATLAB system software system.
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.