Electromagnetic interference signal recognition based on neural network

This article mainly studies the electromagnetic interference signal recognition method based on neural networks. Accurately identifying electromagnetic interference signals is crucial for ensuring the normal operation of equipment in modern electronic systems. Neural networks have become an important tool in the field of electromagnetic interference signal recognition due to their powerful feature learning and classification capabilities. This article elaborates on the principles and applications of commonly used neural network models, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks, in electromagnetic interference signal recognition. It introduces methods for data acquisition and preprocessing, verifies the performance of different models in recognition accuracy, robustness, and other aspects through experiments, and looks forward to future development trends.