The prediction model of blasting vibration has always been a hot and difficult topic because of the very complex nonlinear relationship between the blasting vibration and its influencing factors. A novel algorithm of Nested-ELM for predicting blasting vibration was proposed in this paper. Nested-ELM algorithm can quickly select the optimal input weights and biases of hidden nodes by setting MSE as the fitness function and combining with RWS method. And the algorithm can also quickly determine the optimal number of hidden nodes by setting its initial value according to the empirical formulas and selecting MAPE as the diffusion search index. The feasibility and superiority of Nested-ELM algorithm for predicting blasting vibration were proved by the application of Nested-ELM model on four different types of blasting vibration samples. This paper can provide a novel improved ELM algorithm for predicting blasting vibration with good performance in operation efficiency, prediction accuracy, generalization and sample-number independence.

中文翻译：

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一种预测爆破振动的Nested-ELM新算法

由于爆破振动及其影响因素之间存在非常复杂的非线性关系，爆破振动的预测模型一直是一个热门和难点的课题。本文提出了一种新的Nested-ELM预测爆破振动的算法。Nested-ELM算法通过将MSE设为适应度函数并结合RWS方法，可以快速选择隐藏节点的最优输入权重和偏差。并且该算法还可以通过根据经验公式设置其初始值并选择MAPE作为扩散搜索指标来快速确定最佳隐藏节点数。通过Nested-ELM模型在四种不同类型的爆破振动样本上的应用，证明了Nested-ELM算法预测爆破振动的可行性和优越性。