The incremental extreme learning machine (I-ELM) was proposed in 2006 as a method to improve the network architecture of extreme learning machines (ELMs). To improve on the I-ELM, bidirectional extreme learning machines (B-ELMs) were developed in 2012. The B-ELM uses the same method as the I-ELM but separates the odd and even learning steps. At the odd learning step, a hidden node is added like I-ELM. At the even learning step, a new hidden node is added via a formula based on the former added node result. However, some of the hidden nodes generated by the I-ELM may play a minor role; thus, the increase in network complexity due to the B-ELM may be unnecessary. To avoid this issue, this paper proposes an enhanced B-ELM method (referred to as EB-ELM). Several hidden nodes are randomly generated at each odd learning step, however, only the nodes with the largest residual error reduction will be added to the existing network. Simulation results show that the EB-ELM can obtain higher accuracy and achieve better performance than the B-ELM under the same network architecture. In addition, the EB-ELM can achieve a faster convergence rate than the B-ELM, which means that the EB-ELM has smaller network complexity and faster learning speed than the B-ELM.

中文翻译：

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基于增强随机搜索的改进双向极限学习机

增量式极限学习机（I-ELM）于2006年提出，旨在改善极限学习机（ELM）的网络架构。为了改进I-ELM，2012年开发了双向极限学习机（B-ELM）。B-ELM使用与I-ELM相同的方法，但将奇数和偶数学习步骤分开。在奇怪的学习步骤中，像I-ELM一样添加了一个隐藏节点。在均匀学习步骤中，将基于以前添加的节点结果，通过公式添加新的隐藏节点。但是，由I-ELM生成的某些隐藏节点可能只扮演次要角色；因此，由于B-ELM而导致网络复杂性的增加可能是不必要的。为避免此问题，本文提出了一种增强的B-ELM方法（称为EB-ELM）。在每个奇数学习步骤都会随机生成几个隐藏节点，但是，只有残差减少量最大的节点才会被添加到现有网络中。仿真结果表明，在相同网络架构下，EB-ELM可以获得比B-ELM更高的精度和更好的性能。此外，EB-ELM的收敛速度比B-ELM快，这意味着EB-ELM的网络复杂度比B-ELM小，学习速度也比B-ELM小。