Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In order to select a suitable number of hidden neurons, this paper proposes a novel hybrid learning based on a two-step process. First, the parameters of hidden layer are adjusted by a self-organized learning algorithm. Next, the weights matrix of the output layer is determined using the Moore–Penrose inverse method. Nine classification datasets are considered to demonstrate the efficiency of the proposed approach compared with original extreme learning machine, Tikhonov regularization optimally pruned extreme learning machine, and backpropagation algorithms. The results show that the proposed method is fast and produces better accuracy and generalization performances.

中文翻译：

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基于极限学习机和自组织图的模式分类混合方法。

极限学习机是一种用于单隐藏层前馈神经网络的快速学习算法。但是，隐藏神经元的数量不正确和随机参数对极限学习机的性能影响很大。为了选择合适数量的隐藏神经元，本文提出了一种基于两步过程的新型混合学习方法。首先，通过自组织学习算法来调整隐藏层的参数。接下来，使用Moore-Penrose逆方法确定输出层的权重矩阵。与原始极限学习机，Tikhonov正则化最佳修剪极限学习机和反向传播算法相比，九个分类数据集被认为可证明所提出方法的效率。