极限学习机[1](Extreme Learning Machine,

ELM)是在单隐层神经网络(Single hidden Layer Feed-forward Neural networks, SLFNs)基础上提出的一种高效的学习方法。不同于传统的神经网络，

ELM 中所有的隐层参数均为随机产生，而不需要烦琐的迭代过程；其输出权值则通过求解矩阵的广义逆得到。因此，相较于传统的SLFNs，在保证学习性能的基础上，ELM 的训练速度得以显著提升。-Extreme Learning Machine [1] (Extreme Learning Machine, ELM) is a highly effective way to learn a single hidden layer neural network (Single hidden Layer Feed-forward Neural networks, SLFNs) put forward basis. Unlike traditional neural networks, ELM all the hidden layer parameters are randomly generated, without the need for cumbersome iterative process　their output weights are obtained by solving the generalized inverse matrix obtained. Thus, compared to the traditional SLFNs, learning performance guarantee on the basis of, ELM training speed can be significantly improved.