A method of pattern classification for faint signals
This paper proposes a faint signal processing approach combining AR model and BP neural network (NN), by which the faint signal is fitted with AR model, whose coefficient served as signal eigenvector, and then sent into a three-tier BP NN for training and recognition classification. Classification tests on human pulse signals between drug users and non-users show that this approach is characterized in high speed and high recognition rate.