CAJ | 학술논문

针对经验模态分解过程中产生的端点效应问题，提出了将镜像延拓和支持向量回归机相结合的端点延拓改进方法。利用支持向量回归机对原始信号的极值点数据序列两端进行预测，用镜像延拓法确定所预测极值点的位置。该改进方法解决了支持向量回归机对长数据序列预测不准确，以及镜像延拓法对端点不是极值点的短数据序列处理效果不佳等问题。引入六个评价标准，对端点延拓方法的效果进行了分析。结果表明，该改进方法能有效地抑制经验模态分解产生的端点效应。
침대경험모태분해과정중산생적단점효응문제，제출료장경상연탁화지지향량회귀궤상결합적단점연탁개진방법。이용지지향량회귀궤대원시신호적겁치점수거서렬량단진행예측，용경상연탁법학정소예측겁치점적위치。해개진방법해결료지지향량회귀궤대장수거서렬예측불준학，이급경상연탁법대단점불시겁치점적단수거서렬처리효과불가등문제。인입륙개평개표준，대단점연탁방법적효과진행료분석。결과표명，해개진방법능유효지억제경험모태분해산생적단점효응。
In order to solve the endpoint effect in the empirical mode decomposition, an improved method combining the mirror extension with the Support Vector Regression(SVR)is proposed. In the improved method, the SVR method is applied to predicting extreme points on both ends of the extreme points of the original signal, and then the mirror extension method is applied to determining the position of the predicted extreme points. The improved method can solve the inaccu-rate prediction on the long data sequence by using the SVR method separately, and the problem that the boundary of the short time sequence is not the extreme point by using the mirror extension method separately. At the end, the endpoint extension effect is analyzed by using the evaluation criteria. The simulation results show that the improved method can effec-tively restrain the endpoint effect of the empirical mode decomposition.