A novel algorithm of Nested-ELM for predicting blasting vibration

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.