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| Applying Genetic Algorithms in EMD False Component Identification |
| SONG Na,SHI Yu,ZHOU Ke-yin |
| School of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China |
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Abstract As EMD (Empirical Mode Decomposition) produces false component, the false component was studied by combining genetic algorithm with Kullback-Leibler divergence. First, the original signal was decomposed into several intrinsic mode functions (IMF); the original signal and each IMF component were respectively selected the optimal bandwidth that the genetic algorithm and the optimization principles of bandwidth based on integral mean square error were combined;and then applied kernel density estimation methods to get their probability density function estimation;Finally, the Kullback-Leibler divergence between the original signal and each IMF was calculated, setting the threshold of K-L divergence,IMF component whose K-L divergence is greater than the threshold can be moved. The experiment shows that this method can obtain the bandwidth of experimental data quickly and accurately, the Kullback-Leibler divergence between the real components and the false ones has clearly difference, and the false component can be accurately identified according to the threshold.
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2015, Vol. 36 
