【摘要】 局部均值分解(LMD)是一种新的非线性非平稳信号处理方法,该方法具有较强的自适应性,能将复杂信号分解为一系列具有物理意义的PF(production function)分量。但在信号分解过程中会产生端点效应,这将影响LMD分解精度,严重时会导致信号失真。在详细分析了LMD产生端点效应的原因之后,提出了一种基于相似波形加权匹配的端点延拓算法。通过对视觉诱发脑电信号进行仿真,并且和不做延拓的LMD分解结果做对比,说明该方法能够有效抑制LMD的端点效应,取得较好的分解效果。更多还原

【Abstract】 As a new nonlinear and non-stationary signal analysis method,local mean decomposition(LMD) has a good self-adaptability.This method can decompose complicated signals into a series of physically meaningful production function(PF) components.But performing LMD will produce end effects which make results distorted.After analyzing reasons for end effects of LMD in detail,a new method based on weighted matching similar waveform was proposed.The results from simulation signals of VEP indicate that this method can restrain the end effects effectively and get a good decomposition effect.更多还原