本文主要研究内容
作者方苏阳（2019）在《多源异构预测模型融合方法及其在矿山沉陷预测中的应用》一文中研究指出：开采沉陷预计对指导矿山开采规划、安全生产、“三下”采煤、矿区环境治理具有重要的理论与实践意义,使得开采沉陷预计方法一直是岩层移动控制领域的研究热点。开采沉陷的本质为在地质采矿条件等多因素胁迫下的复杂力学变形过程,其变形过程在时空上表现为高度非线性特征,因而利用非线性预测理论进行开采沉陷预计在理论上具备可行性。文献研究表明,当前已有学者基于非线性预测方法建立了一些开采沉陷预测模型,并初步取得了一定的实验效果,但仍然存在以下问题需要进一步研究:(1)预测模型多为基于单一非线性预测方法而构建,且不适用于采动地表移动全周期(开始期、活跃期和衰退期)的开采预测;(2)鲜有顾及经典非线性预测方法的优缺点互补性,建立多源异构融合的开采沉陷预测模型;(3)构建的非线性开采沉陷预测模型尚未顾及数据新鲜程度的影响,预计模型不够稳健。针对上述问题,本文开展了系统研究,并基于MATLAB平台编制了工程应用程序,研究成果丰富完善了开采沉陷预计理论体系。通过研究,主要取得如下成果:(1)通过研究国内外文献,详细分析了基于岩层移动机理的开采沉陷预测方法以及基于开采沉陷时间特征的非线性预测方法的国内外研究现状。研究表明,当前针对矿山开采沉陷预测方面的研究,多数为矿山开采沉陷全周期进行数值模拟以及预测,少有分阶段精准研究预测模型的案例。因此,开展能够准确揭示矿区开采沉陷变形机理的沉降预测方法需要进一步深入研究。(2)对顾桥北矿观测站实测最大下沉点MS23全周期数据进行预处理,取活跃期以及衰退期点MS23沉降数据建立典型非线性预测模型,通过实验验证开采沉陷不同阶段非线性预测模型的精度以及稳定性,通过定性、定量分析方法对非线性模型进行筛选。结果表明,在开采沉陷活跃期,ARMA预测模型和BP神经网络模型均能相对准确稳定的预测监测点沉降量;在开采沉陷衰退期,BP神经网络预测模型、卡尔曼滤波预测模型、ARMA预测模型以及三次指数平滑模型均能较好的预测监测点沉降量。(3)基于多源异构融合准则以及非线性模型权重确定准则,利用筛选后的非线性预测模型,建立融合多源异构模型。以淮南顾桥北矿实测数据为例,采用非线性预测模型预测值以及实测值进行检验,结果表明,开采沉陷活跃期以最小方差倒数法建立的多源异构模型在预测精度以及稳定上优于单项预测模型;衰退期以熵值法建立的多源异构模型在预测精度以及稳定上优于单项预测模型。因此,多源异构模型能够提取单项非线性预测模型预测信息,并充分融合单项非线性预测模型的优势,能够有效提高预测精度和稳定性。(4)研究顾及矿区开采沉陷特征的数据新鲜度函数,建立顾及数据新鲜度的多源异构模型,预测顾桥北矿最大下沉点MS23活跃期以及衰退期的沉降值,结果表明,由于活跃期开采沉陷发育迅速,沉降量变化明显,考虑实测数据新旧程度能够有效提高多源异构模型的预测精度及稳定性;在衰退期,地表移动变形过程平稳、趋势趋于稳定,顾及数据新鲜度(熵值法)的多源异构模型预测精度略低于多源异构模型,但是提高了预测的稳健性,预测性能满足工程应用需求。图[32]表[36]参[79]
Abstract
kai cai chen xian yu ji dui zhi dao kuang shan kai cai gui hua 、an quan sheng chan 、“san xia ”cai mei 、kuang ou huan jing zhi li ju you chong yao de li lun yu shi jian yi yi ,shi de kai cai chen xian yu ji fang fa yi zhi shi yan ceng yi dong kong zhi ling yu de yan jiu re dian 。kai cai chen xian de ben zhi wei zai de zhi cai kuang tiao jian deng duo yin su xie pai xia de fu za li xue bian xing guo cheng ,ji bian xing guo cheng zai shi kong shang biao xian wei gao du fei xian xing te zheng ,yin er li yong fei xian xing yu ce li lun jin hang kai cai chen xian yu ji zai li lun shang ju bei ke hang xing 。wen suo yan jiu biao ming ,dang qian yi you xue zhe ji yu fei xian xing yu ce fang fa jian li le yi xie kai cai chen xian yu ce mo xing ,bing chu bu qu de le yi ding de shi yan xiao guo ,dan reng ran cun zai yi xia wen ti xu yao jin yi bu yan jiu :(1)yu ce mo xing duo wei ji yu chan yi fei xian xing yu ce fang fa er gou jian ,ju bu kuo yong yu cai dong de biao yi dong quan zhou ji (kai shi ji 、huo yue ji he cui tui ji )de kai cai yu ce ;(2)xian you gu ji jing dian fei xian xing yu ce fang fa de you que dian hu bu xing ,jian li duo yuan yi gou rong ge de kai cai chen xian yu ce mo xing ;(3)gou jian de fei xian xing kai cai chen xian yu ce mo xing shang wei gu ji shu ju xin xian cheng du de ying xiang ,yu ji mo xing bu gou wen jian 。zhen dui shang shu wen ti ,ben wen kai zhan le ji tong yan jiu ,bing ji yu MATLABping tai bian zhi le gong cheng ying yong cheng xu ,yan jiu cheng guo feng fu wan shan le kai cai chen xian yu ji li lun ti ji 。tong guo yan jiu ,zhu yao qu de ru xia cheng guo :(1)tong guo yan jiu guo nei wai wen suo ,xiang xi fen xi le ji yu yan ceng yi dong ji li de kai cai chen xian yu ce fang fa yi ji ji yu kai cai chen xian shi jian te zheng de fei xian xing yu ce fang fa de guo nei wai yan jiu xian zhuang 。yan jiu biao ming ,dang qian zhen dui kuang shan kai cai chen xian yu ce fang mian de yan jiu ,duo shu wei kuang shan kai cai chen xian quan zhou ji jin hang shu zhi mo ni yi ji yu ce ,shao you fen jie duan jing zhun yan jiu yu ce mo xing de an li 。yin ci ,kai zhan neng gou zhun que jie shi kuang ou kai cai chen xian bian xing ji li de chen jiang yu ce fang fa xu yao jin yi bu shen ru yan jiu 。(2)dui gu qiao bei kuang guan ce zhan shi ce zui da xia chen dian MS23quan zhou ji shu ju jin hang yu chu li ,qu huo yue ji yi ji cui tui ji dian MS23chen jiang shu ju jian li dian xing fei xian xing yu ce mo xing ,tong guo shi yan yan zheng kai cai chen xian bu tong jie duan fei xian xing yu ce mo xing de jing du yi ji wen ding xing ,tong guo ding xing 、ding liang fen xi fang fa dui fei xian xing mo xing jin hang shai shua 。jie guo biao ming ,zai kai cai chen xian huo yue ji ,ARMAyu ce mo xing he BPshen jing wang lao mo xing jun neng xiang dui zhun que wen ding de yu ce jian ce dian chen jiang liang ;zai kai cai chen xian cui tui ji ,BPshen jing wang lao yu ce mo xing 、ka er man lv bo yu ce mo xing 、ARMAyu ce mo xing yi ji san ci zhi shu ping hua mo xing jun neng jiao hao de yu ce jian ce dian chen jiang liang 。(3)ji yu duo yuan yi gou rong ge zhun ze yi ji fei xian xing mo xing quan chong que ding zhun ze ,li yong shai shua hou de fei xian xing yu ce mo xing ,jian li rong ge duo yuan yi gou mo xing 。yi huai na gu qiao bei kuang shi ce shu ju wei li ,cai yong fei xian xing yu ce mo xing yu ce zhi yi ji shi ce zhi jin hang jian yan ,jie guo biao ming ,kai cai chen xian huo yue ji yi zui xiao fang cha dao shu fa jian li de duo yuan yi gou mo xing zai yu ce jing du yi ji wen ding shang you yu chan xiang yu ce mo xing ;cui tui ji yi shang zhi fa jian li de duo yuan yi gou mo xing zai yu ce jing du yi ji wen ding shang you yu chan xiang yu ce mo xing 。yin ci ,duo yuan yi gou mo xing neng gou di qu chan xiang fei xian xing yu ce mo xing yu ce xin xi ,bing chong fen rong ge chan xiang fei xian xing yu ce mo xing de you shi ,neng gou you xiao di gao yu ce jing du he wen ding xing 。(4)yan jiu gu ji kuang ou kai cai chen xian te zheng de shu ju xin xian du han shu ,jian li gu ji shu ju xin xian du de duo yuan yi gou mo xing ,yu ce gu qiao bei kuang zui da xia chen dian MS23huo yue ji yi ji cui tui ji de chen jiang zhi ,jie guo biao ming ,you yu huo yue ji kai cai chen xian fa yo xun su ,chen jiang liang bian hua ming xian ,kao lv shi ce shu ju xin jiu cheng du neng gou you xiao di gao duo yuan yi gou mo xing de yu ce jing du ji wen ding xing ;zai cui tui ji ,de biao yi dong bian xing guo cheng ping wen 、qu shi qu yu wen ding ,gu ji shu ju xin xian du (shang zhi fa )de duo yuan yi gou mo xing yu ce jing du lve di yu duo yuan yi gou mo xing ,dan shi di gao le yu ce de wen jian xing ,yu ce xing neng man zu gong cheng ying yong xu qiu 。tu [32]biao [36]can [79]
论文参考文献
论文详细介绍
论文作者分别是来自安徽理工大学的方苏阳，发表于刊物安徽理工大学2019-07-08论文，是一篇关于开采沉陷论文,典型非线性预测模型论文,融合多源异构预测模型论文,新鲜度函数论文,安徽理工大学2019-07-08论文的文章。本文可供学术参考使用，各位学者可以免费参考阅读下载，文章观点不代表本站观点，资料来自安徽理工大学2019-07-08论文网站，若本站收录的文献无意侵犯了您的著作版权，请联系我们删除。
标签：开采沉陷论文; 典型非线性预测模型论文; 融合多源异构预测模型论文; 新鲜度函数论文; 安徽理工大学2019-07-08论文;