Application of Improved Convolutional Neural Network for Active Alarm of Business Center Abnormal Information in Customer Service
A fault diagnosis method based on improved convolutional neural network was proposed to address issues such as delayed manual diagnosis of abnormal information of business center resources in customer service and low fault identification rate.After the convolutional layer,a batch normalization layer was introduced to improve the generalization ability of the model.The Drupout function was introduced in the fully connected layer to alleviate overfitting problems.The data was also enhanced and the grey wolf algorithm was used to optimize hyperparameters.The model was simulated in Python and Pycharm environments.It is found that the test set accuracy of the classical convolutional neural network is around 85％,while the improved test set achieves a result of around 94％,indicating that the proposed design has significant effects.
convolutional neural networkbatch normalizationDropout layergrey wolf algorithmbusiness center resources
万方数据
维普
