中文摘要：

经验模态分解对非线性、非平稳信号进行平稳化处理时表现出特有的分析能力,能够有效获得非平稳信号的时频特征,但是其利用样条曲线构造信号上下包络线的过程中存在严重的端点效应。在分析端点效应产生机理的基础上,提出基于神经网络集成的B样条经验模态分解（B-spline empirical mode decomposition,BS-EMD）端点效应抑制方法,研究神经网络集成延拓的原理,利用神经网络集成对数据进行左延拓和右延拓,利用B样条插值函数对延拓后的数据进行插值计算,得到信号的均值曲线,进行经验模式分解,得到本征模函数。仿真和试验结果表明,该方法能有效抑制BS-EMD的端点效应。

英文摘要：

The empirical mode decomposition （EMD） method presents its own ability for processing nonlinear and non-stationary signals. It can effectively obtain the time-frequency characteristics of non-stationary signals. But there is an involved end effect in the course of getting two envelops of the data using spline interpolation. Based on the consideration of the mechanism of the end effect, a new method for restraining the end effects of B-spline empirical mode decomposition （BS-EMD） based on the neural network ensemble is proposed. The data extension technology based on the neural network ensemble is described. The two ends of the original signal are extended and predicted using the neural network ensemble method. Then, the mean interpolation curve of the extended signal is calculated by B-spline interpolation function. The intrinsic mode functions are calculated by EMD. The results of simulation and practical signal analysis show that the method can restrain the end effects of BS-EMD.