基于优化IGA-ELM模型的爆破振动特征参量预测研究

doi:10.16265/j.cnki.issn1003-3033.2017.11.007

中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (11): 37-42.doi: 10.16265/j.cnki.issn1003-3033.2017.11.007

基于优化IGA-ELM模型的爆破振动特征参量预测研究

温廷新^1,2 教授, 陈晓宇¹, 刘天宇², 刘旭²

1 辽宁工程技术大学系统工程研究所,辽宁葫芦岛 125105
2 辽宁工程技术大学工商管理学院,辽宁葫芦岛 125105

收稿日期:2017-07-03 修回日期:2017-09-20 发布日期:2020-10-21
作者简介:温廷新 (1974—),男,山西太谷人,博士,教授,硕士生导师,主要从事数据挖掘、信息系统、采矿工程等方面的研究。E-mail:wen_tx@163.com。
基金资助:
国家自然科学基金资助(71371091);辽宁省高等学校杰出青年学者成长计划项目(LJQ2012027)。

Predicting research on characteristic parameters of blast-induced vibration based on optimized IGA-ELM model

WEN Tingxin^1,2, CHEN Xiaoyu¹, LIU Tianyu², LIU Xu²

1 System Engineering Institute, Liaoning Technical University, Huludao Liaoning 125105,China
2 School of Business Administration, Liaoning Technical University, Huludao Liaoning 125105,China

Received:2017-07-03 Revised:2017-09-20 Published:2020-10-21

摘要/Abstract

摘要： 为有效预测露天矿爆破振动特征参量,建立基于组合赋权的免疫遗传算法(IGA)优化极限学习机(ELM)预测模型。建立该模型之前,根据爆破振动影响因素确定输入层参数,根据爆破安全规程判据确定输出层参数;然后,应用调和平均数概念整合模糊层次分析法(FAHP)所得主观权重和熵权法所得客观权重,量化输入层参数权重;其次,针对现有ELM输入层权值、隐含层偏差的选择问题,引入IGA对其进行优化选择,并通过逐步增减法探究ELM隐含层最优节点数。该模型曾被应用于某露天矿。研究结果表明:用所构建优化IGA-ELM模型能够更准确地预测露天矿爆破振动特征参量,且所得均方误差、决定系数、仿真误差明显优于其他模型。

关键词: 露天矿, 爆破振动, 特征参量, 组合赋权, 免疫遗传算法(IGA), 极限学习机(ELM)

Abstract: To predict the characteristic parameters for blasting vibration of open-mine effectively, an optimal IGA-ELM model was built on the basis of combination weighting. Before building the model, the parameters of input layer was determined in line with the influence factors of blasting vibration. And that of output layer were confirmed according to the safety regulation criterion for blasting. Then, the subjective and objective weights obtained by fuzzy analytic hierarchy process (FAHP) and entropy weight method respectively were integrated by applying the harmonic mean concept. And the weights of input layer parameters were quantified. In addition, IGA was introduced to select the input layer weights and hidden layer deviations of ELM by optimization. The optimal node number of ELM hidden layer was explored by using the stepwise increase-decrease method.The model was applied to a certain open-mine in China.The research results show that the optimal IGA-ELM model can be used to predict the characteristic parameters for blasting vibration of strip mine more accurately, and that the mean square error, determination coefficient, and simulation error are superior to those obtained by other models.

Key words: open mine, blast-induced vibration, characteristic parameter, combination weighing, immune genetic algorithm(IGA), extreme learning machine(ELM)

中图分类号:

X932

温廷新教授, 陈晓宇, 刘天宇, 刘旭. 基于优化IGA-ELM模型的爆破振动特征参量预测研究[J]. 中国安全科学学报, 2017, 27(11): 37-42.

WEN Tingxin, CHEN Xiaoyu, LIU Tianyu, LIU Xu. Predicting research on characteristic parameters of blast-induced vibration based on optimized IGA-ELM model[J]. China Safety Science Journal, 2017, 27(11): 37-42.

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基于优化IGA-ELM模型的爆破振动特征参量预测研究

Predicting research on characteristic parameters of blast-induced vibration based on optimized IGA-ELM model

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