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采用Monte-Carlo随机模拟方法来研究外部噪声对经验模态分解非线性信号的影响.结果表明:噪声对低阶特征模态函数(IMF)影响较为明显,对高阶IMF影响较小;白噪声强度系数越大,分解出的IMF纯噪声分量阶数越多;用含噪声信号减去经验模态分解后的主要IMF噪声分量,可较为明显地削弱噪声的影响;含噪声响应的最大Lyapunov指数比不含噪声响应的最大Lyapunov指数小
The Monte-Carlo stochastic simulation method is used to study the effect of external noise on the empirical mode decomposition nonlinear signals. The results show that the noise has obvious influence on low-order IMFs and low-order IMFs. The white noise The greater the strength coefficient, the more the decomposed IMF pure noise component order is. The noise signal can be significantly reduced by subtracting the main IMF noise component after the empirical mode decomposition from the noise-containing signal. The maximum Lyapunov exponent with noise response Smaller than the maximum Lyapunov exponent without noise response