利用经验模态分解在处理非线性、非平稳信号以及人工神经网络可以较好地处理非线性问题的优点,通过经验模态分解把加入噪声的仿真信号分解成几个本征模态函数分量和一个趋势项,在分解过程中采用两种方法处理端点效应问题,结果表明两种方法都能很好的解决端点问题,然后对每个分量分别运用径向基函数神经网络进行预测,并重构出最后的预测结果.与不经EMD处理直接运用神经网络进行预测及真实数据进行对比,结果表明,相对于直接预测,该方法具有更好的预测效果.%In view of that the EMD (Empirical Mode Decomposition, referred to as EMD) in dealing with nonlinear, non-stationary signal, and artificial neural network (Artificial Neural Networks, referred to as ANN, also known as neural network) can deal better with the nonlinear problems,we propose a new way combining these two methods for dealing with the signal prediction. Firstly, by using EMD to decompose the simulation signal with noise into several IMF( Intrinsic Mode Function, referred to as IMF) components and a tendency and then in two ways to deal with the endpoint problem in the decomposition process. The results show that the two methods can be good to solve the endpoint problem, then for each component using RBF (Radial Basis Function) neural network to predict separately, and reconstruct the final prediction results. Compared with the use of neural networks to predict directly without being processed with EMD and real data, this method has a higher prediction accuracy.
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