Nonlinear Signal Classification based on Wavelet Transform and Deep Belief Network

In recent years, the analysis of EMI (Electromagnetic interference) signals has become a hot research topic in the signal processing field. Particularly, EMI signal classification has attracted more and more attention. Conventional signal classification methods usually select features by experience or implicitly by the shallow artificial neural network, and always results in bad performance on high-dimensional and nonlinear EMI signals. This paper proposed a novel classification method based on wavelet transform and deep belief network, while wavelet transform method is used to reduce the dimension of high-dimensional signals and deep belief network can extract nonlinear features from EMI signals. Then we apply BP neural network as the classifier. Results on the benchmark dataset have shown the superiority of the proposed method compared with the state-of-the-art approaches.