Patent ID: 11909563
Assignee: BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS
Field: Computer technology (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 3:
4. The method according to claim 1, wherein the training process of the cyclic residual network comprises:
constructing an initial cyclic residual network; wherein, the initial cyclic residual network comprises: a feature extracting module, a feature fusion module, and a feature classification module; the feature extracting module comprises: a first convolutional layer, a first residual stack, and a second residual stack; each of the first and the second residual stacks comprises: a plurality of residual submodules, each of which comprises: a second convolutional layer, a first batch normalization (BN) layer, and a third convolutional layer; the feature fusion module is configured for performing dimension conversion on feature data output by the feature extracting module; the feature classification module comprises: a plurality of GRUs, a first fully connected (FC) layer and a classifier, each GRU of the feature classification module comprising a plurality of hidden layers and a second BN layer;
inputting the sample feature data items and a classification label for the sample feature data items into the initial cyclic residual network;
obtaining, using the initial cyclic residual network, a classification result for the sample feature data items;
calculating a loss function based on a difference between the classification result and the classification label for the sample feature data items;
minimizing the loss function to obtain a minimized loss function;
determining weight parameters for modules in the initial cyclic residual network by using the minimized loss function; and
updating, based on the weight parameters, parameters in the initial cyclic residual network to obtain the cyclic residual network by training.