摘要
Abstract
To effectively excavate the implicit character of gas emission monitoring data,and to prevent the gas dynamical disaster,based on basic principle of Hilbert-Huang transform (HHT) method,the cuckoo search (CS) and extreme learning machine (ELM),the HHT-CS-ELM dynamic prediction model for gas emission quantity was built.The sample series was decomposed into multiple different frequencies intrinsic mode function (IMF) by EMD;the instantaneous frequency of each component was obtained by Hilbert transformation,then divided them into higher frequency and lower frequency;different prediction models were used to predict the IMF;the final prediction results were obtained by superimposing each forecast.This paper took the gas emission monitoring data in a coal of Fenxi Mining Industry as an example to carry out simulation experiment.The results show that:the HHT method can effectively reduce the complexity of the monitoring data,and the minimum relative error is 0.144％,the maximum relative error is 0.388％,the average relative error is 0.281％;this model has higher prediction precision and generalization ability;it can be well applied to non-stationary time series prediction.关键词
绝对瓦斯涌出量/Hilbert变换/布谷鸟搜索算法/极限学习机/时序预测Key words
absolute gas emission quantity/Hilbert transform/cuckoo search algorithm/extreme learning machine/time series prediction分类
矿业与冶金
