中文说明： 极限学习机（extreme learning machine）ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。2004年由南洋理工大学黄广斌副教授提出。传统的神经网络学习算法（如BP算法）需要人为设置大量的网络训练参数，并且很容易产生局部最优解。极限学习机只需要设置网络的隐层节点个数，在算法执行过程中不需要调整网络的输入权值以及隐元的偏置，并且产生唯一的最优解，因此具有学习速度快且泛化性能好的优点。
English Description：
Extreme learning machine (extreme learning machine) ELM is an effective and easy to use, single hidden-layer feedforward neural network learning algorithm SLFNs. 2004 proposed by Huang Guangbin, an associate professor at Nanyang Technological University. Traditional neural network learning algorithms (such as BP algorithm) manual setting parameters of training, and it is easy to produce local optimal solutions. Extreme learning machine only needs to set the number of hidden nodes of the network, the algorithm does not need to be adjusted to the implementation process, the input power values, and implicit bias, and produce a single optimal solution, so has the advantage of fast learning and generalization performance.