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Article(id=1322595200619360980, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322594813749350688, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1710345600000, receivedDateStr=2024-03-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793260868458, onlineDateStr=2026-10-29, pubDate=1740412800000, pubDateStr=2025-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793260868458, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793260868458, creator=13701087609, updateTime=1793260868458, updator=13701087609, issue=Issue{id=1322594813749350688, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='2', pageStart='1', pageEnd='288', issueExtLink='null', onlineDate='null', pubDate='1740412800000', pubDateStr='2025-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793260776220, creator='13701087609', updateTime=1793260776220, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=155, endPage=161, ext={EN=ArticleExt(id=1322595200778744533, articleId=1322595200619360980, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Risk Identification of Multi-Source Index in Goaf Based on Optimized Extreme Learning Machine Model, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to improve the prediction accuracy of multi-source index risk identification in goaf, based on the particle swarm optimization (PSO) algorithm to optimize the input weights and thresholds of the extreme learning machine (ELM), a PSO-ELM method was proposed to identify the risk of multi-source indicators in the goaf. Nine indicators, including mining depth, mining method, goaf height, the maximum exposed area, the maximum exposed height, the maximum exposed span, mining pillars, measured volume, and control rate, which affect the risk of goaf in a mine in South China, were used as the main input values, and 135 measured goaf data were used as an example to predict the hazard level of goaf. The research results show that the prediction accuracy of radial basis function and traditional extreme learning machine is 88% and 92%, respectively, while the prediction accuracy of the extreme learning machine model optimized by particle swarm optimization algorithm is 96%. Compared with radial basis functions and traditional extreme learning machines, the PSO-ELM algorithm performs better on the test sets, has a better stability and generalization ability, possess good guiding significance and practicality, and is suitable for practical engineering applications.

, authors=Chengzhe SU1, Yangyao ZOU2, Dianming ZHANG3, authorsList=Chengzhe SU, Yangyao ZOU, Dianming ZHANG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322595207921644282, articleId=1322595200619360980, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于优化极限学习机模型的采空区多源指标危险性辨识研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

为了提高对采空区多源指标危险性辨识的预测精度，基于粒子群(PSO)算法优化极限学习机(ELM)的输入权重和阈值，提出一种PSO-ELM的方法对采空区多源指标危险性进行辨识。采用影响华南某矿山采空区危险性的开采深度、采矿方式、采空区采高、采空区最大暴露面积、采空区最大暴露高度、采空区最大暴露跨度、矿区矿柱、实测体积和治理率9项指标作为主要输入值，并以135个实测采空区数据为例进行危险等级预测。研究结果表明：径向基函数和传统极限学习机的预测精度分别为88%和92%，而经过粒子群算法优化的极限学习机模型的预测精度为96%。与径向基函数和传统极限学习机相比，PSO-ELM算法在测试集表现得更好，稳定性和泛化能力更优良，具备良好的指导意义和实用性，适用于实际工程应用。

, authors=苏成哲1, 邹阳耀2, 张殿明3, authorsList=苏成哲, 邹阳耀, 张殿明, authorCompany=null, correspAuthors=null, authorNote=

苏成哲（1985—），男，江西萍乡人，硕士，正高级工程师，主要从事矿山采掘和通风研究。E-mail:

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苏成哲（1985—），男，江西萍乡人，硕士，正高级工程师，主要从事矿山采掘和通风研究。E-mail:

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苏成哲（1985—），男，江西萍乡人，硕士，正高级工程师，主要从事矿山采掘和通风研究。E-mail:

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Quantitative rules for qualitative indicators

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赋值采矿方式矿柱情形
–1中深孔法—
0浅采法无矿柱
1—边界矿柱
2—混合矿柱
), ArticleFig(id=1322595213349073714, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595200619360980, language=CN, label=表1, caption=

定性指标量化规则

, figureFileSmall=null, figureFileBig=null, tableContent=
赋值采矿方式矿柱情形
–1中深孔法—
0浅采法无矿柱
1—边界矿柱
2—混合矿柱
), ArticleFig(id=1322595213424571187, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595200619360980, language=EN, label=Table 2, caption=

Factors affecting the risk of goaf and samples of some hazard levels

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序号Y1/mY2Y3/mY4/m2Y5/mY6/mY7Y8/m3Y9/%危险等级
11301353 589.035.0039057 481.101
21301201 208.00.9924112 141.394.41
31301351 735.05.9728031 595.796.31
41301351 644.035.0032217 144.4100.01
51301252 489.525.0039219 377.7100.01
……
134308–1151 365.08.0034013 568.069.21
135336–1141 052.014.0021014 846.001
), ArticleFig(id=1322595213512651572, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595200619360980, language=CN, label=表2, caption=

采空区危险性影响因素及部分危险等级样本

, figureFileSmall=null, figureFileBig=null, tableContent=
序号Y1/mY2Y3/mY4/m2Y5/mY6/mY7Y8/m3Y9/%危险等级
11301353 589.035.0039057 481.101
21301201 208.00.9924112 141.394.41
31301351 735.05.9728031 595.796.31
41301351 644.035.0032217 144.4100.01
51301252 489.525.0039219 377.7100.01
……
134308–1151 365.08.0034013 568.069.21
135336–1141 052.014.0021014 846.001
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基于优化极限学习机模型的采空区多源指标危险性辨识研究
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苏成哲 1 , 邹阳耀 2 , 张殿明 3
矿业研究与开发 | 矿山安全与环保 2025,45(2): 155-161
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矿业研究与开发 |矿山安全与环保 2025 , 45 (2) : 155 -161
基于优化极限学习机模型的采空区多源指标危险性辨识研究
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苏成哲（1985—），男，江西萍乡人，硕士，正高级工程师，主要从事矿山采掘和通风研究。E-mail:

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苏成哲（1985—），男，江西萍乡人，硕士，正高级工程师，主要从事矿山采掘和通风研究。E-mail:

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苏成哲1 , 邹阳耀2, 张殿明3
作者信息
  • 1.兰州有色冶金设计研究院有限公司，甘肃　兰州　730099
  • 2.合肥工业大学 土木与水利工程学院，安徽　合肥　230009
  • 3.新疆西拓矿业有限公司，新疆　哈密市　839000
作者简介:

苏成哲（1985—），男，江西萍乡人，硕士，正高级工程师，主要从事矿山采掘和通风研究。E-mail:

Risk Identification of Multi-Source Index in Goaf Based on Optimized Extreme Learning Machine Model
Chengzhe SU1 , Yangyao ZOU2, Dianming ZHANG3
Affiliations
  • 1.Lanzhou Nonferrous Metallurgy Design and Research Institute Co., Ltd., Lanzhou, Gansu 730099, China
  • 2.School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei, Anhui 230009, China
  • 3.Xinjiang Xituo Mining Co., Ltd., Hami, Xinjiang 839000, China
出版时间: 2025-02-25
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摘要
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为了提高对采空区多源指标危险性辨识的预测精度，基于粒子群(PSO)算法优化极限学习机(ELM)的输入权重和阈值，提出一种PSO-ELM的方法对采空区多源指标危险性进行辨识。采用影响华南某矿山采空区危险性的开采深度、采矿方式、采空区采高、采空区最大暴露面积、采空区最大暴露高度、采空区最大暴露跨度、矿区矿柱、实测体积和治理率9项指标作为主要输入值，并以135个实测采空区数据为例进行危险等级预测。研究结果表明：径向基函数和传统极限学习机的预测精度分别为88%和92%，而经过粒子群算法优化的极限学习机模型的预测精度为96%。与径向基函数和传统极限学习机相比，PSO-ELM算法在测试集表现得更好，稳定性和泛化能力更优良，具备良好的指导意义和实用性，适用于实际工程应用。

径向基函数  /  粒子群算法  /  极限学习机  /  采空区  /  危险性评价

In order to improve the prediction accuracy of multi-source index risk identification in goaf, based on the particle swarm optimization (PSO) algorithm to optimize the input weights and thresholds of the extreme learning machine (ELM), a PSO-ELM method was proposed to identify the risk of multi-source indicators in the goaf. Nine indicators, including mining depth, mining method, goaf height, the maximum exposed area, the maximum exposed height, the maximum exposed span, mining pillars, measured volume, and control rate, which affect the risk of goaf in a mine in South China, were used as the main input values, and 135 measured goaf data were used as an example to predict the hazard level of goaf. The research results show that the prediction accuracy of radial basis function and traditional extreme learning machine is 88% and 92%, respectively, while the prediction accuracy of the extreme learning machine model optimized by particle swarm optimization algorithm is 96%. Compared with radial basis functions and traditional extreme learning machines, the PSO-ELM algorithm performs better on the test sets, has a better stability and generalization ability, possess good guiding significance and practicality, and is suitable for practical engineering applications.

Radial basis function  /  Particle swarm optimization  /  Extreme learning machine  /  Goaf  /  Risk assessment
苏成哲, 邹阳耀, 张殿明. 基于优化极限学习机模型的采空区多源指标危险性辨识研究. 矿业研究与开发, 2025 , 45 (2) : 155 -161 .
Chengzhe SU, Yangyao ZOU, Dianming ZHANG. Risk Identification of Multi-Source Index in Goaf Based on Optimized Extreme Learning Machine Model[J]. Mining Research and Development, 2025 , 45 (2) : 155 -161 .
正文
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0　引言
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随着铁矿开采量日益增加，矿山的采空区范围越来越大，受人为或自然因素影响，采空区可能发生沉降或塌陷，从而导致矿山安全事故[1-3]。因此，对采空区危险源进行辨识研究，提前进行风险预警和加固处理，对于排除生产安全隐患，降低安全风险意义重大。
陈昌民等[4]通过传统的经验公式计算了采场爆破设计参数的范围，设计并使用容重、弹性模量、抗压强度等多个指标作为矿岩输入值，将炸药的单耗、排距、孔底距离3个指标作为输出值，并将此研究方法成功地运用于7个铁矿山的深孔爆破开采中，建立了基于极限学习机的爆破设计参数优化的回采预测模型。FU等[5]基于赤铁矿的光谱和具有随机值与密集连接的多个隐藏层极限学习机，将所有先前层的输出作为每个隐藏层的输入，构建了高精度模型，该模型在赤铁矿的铁含量检测中实现了较高的精度，满足了绿色、智能化新型矿山的建设与发展要求。MARWALA等[6]在1985年1月至2011年12月南非采矿业总生产指数和总用电量的基础上，基于最优剪枝和基本的极限学习机(OP-ELM)建立了非线性模型，该模型显示，采矿生产到电力负荷之间存在单向格兰杰因果关系。DU等[7]利用温度、大气压、累积降雨量、相对湿度和折射率等多种气象影响因子数据，评价了一种基于高斯核函数的极限学习机(KELM)算法在矿山边坡地表变形预测中的有效性，并将KELM模型应用于中煤平朔集团股份有限公司安家岭露天矿的变形分析中，有效地表达了露天矿边坡地表变形与各种气象影响因素之间的非线性关系。LIU等[8]基于横切应力的收敛数据提出了一种优化后的极限学习机算法预测模型，该模型消除了输入权值的随机选择，比传统的极限学习机模型具有较好的泛化能力；以辽宁省前山铁矿地下矿山横切施工为例，通过将预测应力数据与现场实测数据进行比较，验证了改进的极限学习机算法的准确性和高效性。YANG等[9]为了准确快速地预测巷道推进工作面前方煤层的残余瓦斯含量，提出了一种基于钻屑指标和蝙蝠算法优化极限学习机(BA-ELM)的改进预测方法，并将该方法的预测结果与BP神经网络、支持向量机(SVM)和极限学习机(ELM)的预测结果进行比较，验证该方法的准确性，相比其他方法，该方法具有更高的精度，能有效地揭示钻屑指标与残余气含量之间的非线性关系。JING等[10]提出了一种基于极限学习机模型的机器学习方法，该方法可以实时地确定工作性能与作业参数之间的关系以及开挖区域的岩土物理力学性质，达到开挖率更高、作业环境更安全和地面扰动最小的目的，并突破行业长期依赖经验决策模型产生的领域知识碎片化和信息孤岛。LI等[11]提出了一种基于极限学习机和改进的Harris Hawks的岩爆预测方法，该方法具有较快的收敛速度和较高的预测精度，为岩爆烈度预测提供了一条新的途径和方法。RAJA等[12]使用了一种具有成本效益的极限学习机模型来获得更加真实的GRSSF的UBC预测，该模型在预测GRSSF的UBC方面具有一定的实用性和准确性，还可以应用于岩土工程智能系统中进行多元非线性问题的预测。
虽然极限学习机解决了许多矿山地下工程、矿山边坡工程和土木工程等方面的实际问题，但是极限学习机神经网络的输入权重和阈值具有一定的随机性和主观性，对采空区危险源的辨识能力影响较大。为了提高ELM的性能，本文采用PSO算法优化ELM的输入权重和阈值，获取其最佳参数组合，建立基于PSO-ELM的采空区危险辨识模型，并对华南某铁矿的采空区危险等级进行预测，研究结果表明，PSO-ELM具有良好的泛化能力与可靠性，可以为类似采空区危险等级预测及风险预警提供参考价值。
1　径向基函数
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径向基函数(RBF)是一种实值函数，其输出值仅依赖于输入空间中样本点到原点的欧氏距离。这种性质使得径向基函数非常适合处理模式识别和函数逼近等问题。在n维欧式空间中，任意一点X到某一中点C的欧式距离表示为‖X−C‖，则径向基函数φ(‖X−C‖)仅与这一距离有关。径向基函数的一个重要性质是随着‖X−C‖的增大，φ(‖X−C‖)的值会减小。
径向基函数网络以其简洁的数学表达和快速的训练而被广泛应用于函数逼近和数值分析等多个领域。RBF网络的性能受到多个参数的影响，包括基函数的形状参数、中心点的位置和数量等。这些参数的选择和优化对于提高RBF网络的性能至关重要。在数值计算方面，径向基函数主要用于求解偏微分方程、积分方程和插值问题。通过构造适当的RBF网络，可以实现对这些数学问题的数值求解。
Powell于1987年提出了径向基函数[13]，可以转换多变量离散型数据，以便于比较分布模式的优劣。径向基函数通过计算输入数据点与其他点之间的距离，利用这些距离作为权重来进行插值或外推，最终得到输出结果。径向基函数是一种实值函数，其取值只与自变量与中心之间的距离有关，由于其特性，径向基函数被广泛用于矿业建模领域。径向基函数的一般表达式如式(1)所示。
式中：X为自变量；C为径向基函数中心；‖X−C‖为向量范数。
2　粒子群模型
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粒子群算法是模拟鸟类觅食行为的一种全局随机搜索算法[14-15]，其原理是随机生成一组解，通过建立粒子的速度-位置模型，并利用函数适应度评估粒子的优劣程度。在迭代过程中，粒子通过空间搜索来寻找最优参数选择。图1展示了粒子A和粒子B初始时位于曲线两侧，它们通过向曲线中部移动来寻找最低点。如果粒子A的位置高于粒子B的位置，粒子A将朝着粒子B移动；反之，粒子B将向着A移动。当粒子A到达曲线的最低点时，粒子B将一直向A移动，直至在最低点处重合。
粒子群算法的参数优化能力，是通过建立适度函数计算适应值，以验证粒子群算法中粒子的优劣。然后通过粒子的更新，以保证粒子趋于全局最优。其中每个粒子具有速度和位置两个基本信息，Vij(t)代表粒子的速度，xij(t)代表粒子的位置。在寻优过程中，粒子群会进行学习记忆，记忆中包含个体极值和全局极值。个体极值Pij(t)表示粒子经历的最优位置，全局极值gij(t)则表示整体经历的历史最佳位置。每个粒子同时会在个体极值和全局极值的基础上，依据平行四边形法则，更新其速度Vij(t+1)和位置xij(t+1)。粒子通过空间中不断迭代更新自身速度和位置，逐渐接近最优解，如图2所示。
此时，粒子的状态转移方程表示为：
式中：w为惯性权重；c为学习因子；r为在[0,1]区间内变化的随机数。
在粒子群算法中，粒子能够自我调整方向和速度，从而逐步靠近最佳位置。由于该算法具有较快的迭代速度，能够在全局性搜索中取得良好的结果，并且在解决非线性问题时也能表现出较好的优化效果。因此，本文将粒子群算法用于优化极限学习机的输入权重和阈值，以便获取最佳参数组合。
3　极限学习机
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极限学习机是一种单隐含层的前馈神经网络算法[16-17]。与传统神经网络相比，极限学习机在保证学习精度的前提下，其学习速度更快且泛化能力更强，被广泛用于解决各种问题，其原理如下：
假设一个单隐含层神经网络有N个样本(xi,ti)，其中xi=[xi1,xi2,⋯,xin]T∈Rn为输入数据，ti=[ti1,ti2,⋯,tim]T∈Rm为输出数据。
对于单隐含层有L个神经元的神经网络可以表示为：
式中：βi为输出权重；g(x)为激活函数；Wi=[ωi1,ωi2,⋯,ωin]T为输入权重；bi是隐含层第i个神经元的阈值；oj为计算输出值。
当输出值与期望输出值之间误差最小时，即，则式（4）可以简化为矩阵形式：
式中，H,β,T的定义如下：
由于极限学习机的输入权重和隐含层阈值具有一定随机性，对极限学习机的辨识能力影响较大。为提高极限学习机的性能，采用粒子群算法优化极限学习机的初始权重和阈值，得到最优组合参数，并输入到极限学习机模型超参数中，对测试集的采空区等级进行预测，最终建立基于PSO-ELM组合算法的采空区危险等级预测模型。
4　工程实例分析
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本文以华南某铁矿为例，该铁矿经过长期开采已形成了一定规模的采空区。采空区的存在对矿山的安全生产和周边环境都带来了潜在的风险。本文采用PSO-ELM模型对华南某铁矿的采空区危险等级进行预测，以验证PSO-ELM模型的可靠性和泛化能力。
4.1　采空区评价指标
采空区的稳定性受到诸多因素的影响，其中包括采空区的时间、形状、治理方式、暴露面积和高度、围岩强度、护顶层和护底层的厚度、地质构造情况、地下水位及结构面的规模和分布等。此外，还有采空区周边地质环境的变化、采矿活动的振动影响、地应力状态变化以及采矿工艺等因素也会对采空区稳定性产生不利影响，并且这些危险性影响因素对于不同矿山采空区稳定性的影响程度还存在差异。
本文综合相关研究成果[18-20]，选取辨识矿山采空区危险性的9项因素作为采空区危险评价指标，分别为：开采深度(Y1)、采矿方式(Y2)、采空区采高(Y3)、采空区最大暴露面积(Y4)、采空区最大暴露高度(Y5)、采空区最大暴露跨度(Y6)、矿区矿柱(Y7)、实测体积(Y8)和治理率(Y9)。以这9个指标作为模型输入因素，采空区危险等级作为模型输出因素。为方便模型训练，首先对定性指标进行量化处理，处理方式见表1。
本文以135组华南某铁矿采空区实测数据为基础，设置了随机种子打乱采空区样本数据集，以其中前110个样本作为模型的训练集数据，后25个样本作为模型测试集数据，部分样本数据见表2。
4.2　数据预处理
样本数据尺寸大小对模型有较大的影响，导致分类结果精确度降低，在模型训练之前，需要对样本数据进行归一化处理，以消除量纲的影响，归一化公式如式(7)所示：
式中：xij为原始样本数据；Sij为归一化后的数据；min(xj)为某指标数据的最小值；max(xj)为某指标数据的最大值。
4.3　结果分析
PSO-ELM模型经过训练后，得到的模型迭代误差变化曲线如图3所示。由图3可知：经过8次迭代后，适应度值不再变化，表明寻优得到了最优组合参数。
粒子群算法寻优获得的参数组合后输入到极限学习机(ELM)超参数中，通过测试集数据对采空区危险等级进行预测，其预测结果与混淆矩阵如图4和图5所示。
由图4和图5分析可知，PSO-ELM模型在测试集的预测准确率达到96%，为进一步研究PSO-ELM采空区危险等级的性能，本文采用径向基函数(RBF)以及传统的极限学习机(ELM)分别对训练集数据样本进行学习，对测试集采空区等级进行预测，预测结果与混淆矩阵如图6至图9所示。
由图6和图7分析可得，径向基函数(RBF)在测试集上出现3个样本预测错误，预测准确率为88%，表明径向基函数(RBF)模型的泛化能力与可靠性较为一般。
由图8和图9分析可知，未经优化的极限学习机模型在测试集上出现2个样本预测错误，预测准确率达到92%，研究结果表明：对比径向基函数(RBF)模型，极限学习机的泛化能力与可靠性有所提升，但由于极限学习机输入权重和隐含层阈值的随机性，影响了其对采空区危险等级的辨识能力，而经过粒子群优化的极限学习机模型克服了上述缺陷，泛化能力与可靠性都得到了明显改善。
通过PSO-ELM模型与其RBF、ELM的精度对比，RBF以及传统的ELM预测精度分别为88%、92%，而经过PSO优化的ELM采空区危险等级预测模型的预测精度为96%，PSO-ELM模型具有较好的泛化能力，如图10所示。PSO-ELM模型在华南某铁矿采空区的预测结果为Ⅰ级与Ⅱ级，属于轻微风险型采空区，对于暴露面积与体积较大的采空区，应采取合理的支护和加固措施，如岩体注浆、锚杆支护和岩爆控制等，以增强采空区的稳定性。同时，在采空区周边设置支护结构或者填充固体材料，可以有效减少地表沉降和塌陷的风险，提高采空区的整体稳定性。在日常的安全生产活动中可以采取定期监测和检查措施，及时发现并处理可能存在的安全隐患，确保采空区稳定。
5　结论
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由于极限学习机的输入权重和阈值具有一定的随机性，本文采用粒子群算法对其进行优化，提出一种 PSO-ELM组合算法的采空区危险等级辨识方法，并对华南某铁矿的采空区危险等级进行预测，以验证PSO-ELM 模型的可靠性和泛化能力。研究得到的主要结论如下。
（1）基于粒子群(PSO)优化极限学习机(ELM)的输入权重和阈值，提出了一种PSO-ELM神经网络的采空区多源指标危险性辨识方法。
（2）采用径向基函数、传统的极限学习机和优化的极限学习机模型分别对华南某铁矿的采空区危险等级进行了预测。研究结果表明：华南某铁矿采空区整体安全稳定；径向基函数以及传统的极限学习机的预测精度分别为88%和92%，而经过PSO优化的ELM采空区危险等级预测模型的预测精度高达96%，PSO-ELM模型具有更好的泛化能力与可靠性。
（3）通过3种模型预测结果的对比分析可知，优化后的极限学习机神经网络（PSO-ELM)具有更加明显的优势，为采空区危险等级辨识研究提供了新思路，具有良好的工程应用价值。
基金
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  • 深部煤矿采动响应与灾害防控国家重点实验室开放基金项目(SKLMRDPC22KF02)
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2025年第45卷第2期
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  • 接收时间：2024-03-14
  • 首发时间：2026-10-29
  • 出版时间：2025-02-25
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  • 收稿日期：2024-03-14
基金
深部煤矿采动响应与灾害防控国家重点实验室开放基金项目(SKLMRDPC22KF02)
作者信息
    1.兰州有色冶金设计研究院有限公司，甘肃　兰州　730099
    2.合肥工业大学 土木与水利工程学院，安徽　合肥　230009
    3.新疆西拓矿业有限公司，新疆　哈密市　839000
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