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Periodic signal recognition algorithm and quantization based on BP neural network

Aiming at the low accuracy of the traditional periodic signal recognition algorithm and the complexity of recognition circuit, this paper utilizes a lightweight back-propagation neural network (BPNN) model. The model achieves self-learning of the digital characteristics of four periodic signals, avoids the feature extraction under the traditional recognition algorithm, improves the accuracy of recognition , simplifies the structure of the circuit, reduces the cost and difficulty of hardware design. The trained neural network model has a recognition accuracy of 99% on the test dataset. By using 8-bit quantization algorithm inspired by the IEEE-754, the model capacity is reduced to 1/8 of the original, and the model recognition rate is reduced by only 1.3%.