This paper introduced the general model structure of wavelet neural networks
(WNN) for signal recognition and classification. By making full use of the advantages of
wavelet transform time-frequency localization, the paper proposed an improved network
structure and presented a learning algorithm for the hidden functionwavelet neural network. A
simulation on computer showed that using the structure, the precision and sensitivity of signal
recognition and classification were improved.