CAJ | 학술논문

针对 EMD（经验模态分解）产生虚假分量这一问题，将遗传算法和 K-L 散度相结合，对虚假分量进行研究。该方法是先将原始信号进行 EMD 得到固有模态分量（IMF）；将遗传算法和基于均方积分误差的窗宽最优化原则相结合，分别对原始信号和各个 IMF 分量优化选取窗宽；然后运用核密度估计方法分别得到它们的概率密度函数估计；最后计算原始信号与 IMF 分量之间的 K-L 散度值，设定 K-L 阈值，将 K-L 散度值大于阈值的 IMF 分量去除。实验证明，该方法能准确而又快速地获得实验数据的窗宽，虚假成分与真实分量的 K-L 值有明显差别，根据设定的阈值能准确识别虚假分量。
침대 EMD（경험모태분해）산생허가분량저일문제，장유전산법화 K-L 산도상결합，대허가분량진행연구。해방법시선장원시신호진행 EMD 득도고유모태분량（IMF）；장유전산법화기우균방적분오차적창관최우화원칙상결합，분별대원시신호화각개 IMF 분량우화선취창관；연후운용핵밀도고계방법분별득도타문적개솔밀도함수고계；최후계산원시신호여 IMF 분량지간적 K-L 산도치，설정 K-L 역치，장 K-L 산도치대우역치적 IMF 분량거제。실험증명，해방법능준학이우쾌속지획득실험수거적창관，허가성분여진실분량적 K-L 치유명현차별，근거설정적역치능준학식별허가분량。
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 ezperiment shows that this method can obtain the bandwidth of ezperimental 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.