Process data fault classification method based on pseudo label method and weak supervised learning
A technology for labeling data and fault classification, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as false labels and mislabeling
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[0062] The fault classification method based on weakly supervised learning of the present invention will be further described in detail below in combination with specific implementation methods.
[0063] A fault classification method based on pseudo-label method and weakly supervised learning. The training process of this method based on pseudo-label method and weakly supervised learning can be divided into two stages:
[0064] (1) MLP labeled sample learning stage based on pseudo-label method
[0065] The MLP network pairs a labeled sample set D l_std Perform supervised training and use the cross-entropy loss function:
[0066]
[0067] in,(.) T represents a transpose operation, is the representation of the last layer of the MLP network, and θ is the MLP network parameter.
[0068] The loss adjusts the parameters of the entire MLP network through the backpropagation algorithm (BP). After multiple iterations of loss convergence, the optimal parameters of the entire net...
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