Wavelet Packet Energy-Entropy Feature Extraction and Principal Component Analysis for Signal Classification
Marcus Vinicius Monteiro Varanis, Robson Pederiva · Proceeding Series of the Brazilian Society of Computational and Applied Mathematics · 2015
This paper has the usage of energy and entropy parameters associated with Wavelet Packet Transform (WPT) as the target to the automatic signal classification as well as the detection of voltage disturbances in electric signals. One can apply Wavelet Packet Transform to remove noise presented in the signals by means of decomposition to obtain the energy and entropy characteristics. Principal component analysis (PCA) is used to reduce the dimensions of the parameters vector and to classify the kinds of signal and disturbances presented using k- nearest neighbor (kNN). Numerical simulations showed the effectiveness of the proposed method.