中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (11): 38-46.doi: 10.16265/j.cnki.issn1003-3033.2022.11.1915

• 安全工程技术 • 上一篇    下一篇

基于海林格距离和AHDPSO-ELM的岩爆烈度等级预测模型

温廷新(), 陈依琳   

  1. 辽宁工程技术大学 工商管理学院,辽宁 葫芦岛 125105
  • 收稿日期:2022-05-15 修回日期:2022-09-08 出版日期:2022-11-28 发布日期:2023-05-28
  • 作者简介:

    温廷新 (1974—),男,山西太谷人,博士,教授,主要从事矿业系统工程、数据分析与智能决策等方面的研究。E-mail:

  • 基金资助:
    国家自然科学基金资助(71371091); 辽宁省社会科学规划基金资助(L14BTJ004)

Prediction model of rockburst intensity grade based on Hellinger distance and AHDPSO-ELM

WEN Tingxin(), CHEN Yilin   

  1. School of Business Administration, Liaoning Technical University, Huludao Liaoning 125105, China
  • Received:2022-05-15 Revised:2022-09-08 Online:2022-11-28 Published:2023-05-28

摘要:

为提高岩爆烈度等级预测准确率,提出一种基于海林格距离过采样(HDO)和自适应混合差分粒子群优化算法(AHDPSO)-极限学习机(ELM)的预测模型。首先,在分析影响岩爆烈度因素基础上选取主要影响指标,采用HDO算法增加少数类样本数目,均衡各等级岩爆样本;然后,基于粒子群优化(PSO)算法,引入自适应种群间距和差分进化(DE)算法中变异算子设计AHDPSO,利用AHDPSO优选ELM的输入层权值和隐藏层阈值,构建岩爆烈度等级预测模型;最后,采用国内外301组岩爆样本对模型训练、测试并与其他模型对比。研究表明:经HDO算法均衡岩爆数据集后,整体的预测准确率提高11.91%,且各等级的平均预测准确率均得到提高;基于HDO的AHDPSO-ELM岩爆烈度等级预测模型平均预测准确率为98.92%,均方误差为0.010 8,预测精度优于其他对比模型。

关键词: 海林格距离过采样(HDO), 自适应混合差分粒子群优化(AHDPSO), 岩爆烈度等级预测, 极限学习机(ELM), 岩爆样本, 变异算子, 自适应种群间距

Abstract:

In order to improve the prediction accuracy of rockburst intensity grade, a prediction model based on HDO and AHDPSO-ELM was proposed. Firstly, the main evaluation indexes were selected based on the analysis of the influencing factors of rockburst intensity. The HDO algorithm was used to increase the number of minority samples and balance the rockburst samples of each intensity grade. Then, based on particle swarm optimization (PSO), the adaptive population spacing and mutation operator in differential evolution algorithm (DE) were introduced to design AHDPSO. AHDPSO optimized the input layer weight and hidden layer threshold of ELM, and the rockburst grade prediction model was constructed. Finally, 301 sets of rockburst samples at home and abroad were used to train, test, and compare with other models. The results show that after improving the structure of datasets by the HDO algorithm, the overall average accuracy of rockburst prediction is increased by 11.91%, and the average prediction accuracy of each grade has been improved. The average prediction accuracy of the AHDPSO-ELM rockburst intensity prediction model based on HDO is 98.92%, and the mean square error is 0.010 8. The prediction accuracy is better than other comparison models.

Key words: Hellinger distance oversampling (HDO), adaptive hybrid differential particle swarm optimization (AHDPSO), prediction of rockburst intensity grade, extreme learning machine (ELM), rockburst samples, mutation operator, adaptive population spacing