中文摘要：

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

英文摘要：

The Monte-Carlo method is used to investigate the effect of random noise on empirical mode decomposition of the nonlinear signals. The simulation results show that,the influence of noise is obvious for the low-level intrinsic mode function and unabvious for the high-level intrinsic mode function. With the increase of the intensity of white noise,the intrinsic mode function pure noise level will increase. When the intrinsic mode function pure noise levels is subtracted from the noise signal,about 80% of the noise influence will be reduced. The noise signal＇s largest Lyapunov exponent is smaller than that of the noise-free signal.