基于混合蛙跳算法的概率积分模型参数反演  

Parameter inversion of probability integral prediction based on shuffled frog leaping algorithm

作　　者：滕超群 王磊 魏鹏 李靖宇 江克贵 朱尚军 TENG Chaoqun;WANG Lei;WEI Peng;LI Jingyu;JIANG Kegui;ZHU Shangjun(School of Geomatics,Anhui University of Science and Technology,Huainan 232001,China;Resources and Environment Administration,Shanxi Jincheng Anthracite Mining Group Co.,Ltd.,Jincheng 048006,China)

机构地区：[1]安徽理工大学测绘学院,安徽淮南232001 [2]山西晋城无烟煤矿业集团有限责任公司资源环境管理局,山西晋城048006

出　　处：《采矿与岩层控制工程学报》2020年第4期102-108,共7页Journal of Mining and Strata Control Engineering

基　　金：国家自然科学基金资助项目(41602357);安徽高校自然科学研究资助项目(KJ2016A190);江苏省资源环境信息工程重点实验室开放基金资助项目(JS201801)。

摘　　要：基于实测资料精准估计概率积分参数是概率积分函数模型应用的难点。SFLA(混合蛙跳算法)是群体智能优化算法,将SFLA应用于概率积分参数反演中,构建了基于SFLA的概率积分参数估计方法。研究结果表明:①模拟试验中,SFLA反演概率积分预测参数q,tanβ,b,θ的参数估计相对误差分别为0.12%,0.10%,0.11%,0.21%;S_1,S_2,S_3,S_4参数估计相对误差最大不超过3%。②利用此方法求解顾桥南矿1414(1)工作面概率积分参数,求解结果为:q=0.97,tanβ=1.98,b=0.39,θ=86.8°,S_1=-5.07 m,S_2=-17.84 m,S_3=58.01 m,S_4=36.38 m,下沉与水平移动拟合中误差为109.31 mm。Estimating the parameters of the probability integral function based on the measured data is the difficulty of the application of the probability integral function model.SFLA(shuffled frog leaping algorithm)is a group intelligent optimization algorithm.In this paper,SFLA has been used to study the inversion of probability integral parameters for the first time.An estimation method of probability integral parameters based on SFLA was constructed.The results show that:①in the simulation experiment,the estimated relative errors of parameters q,tanβ,b andθof SFLA inversion probability integral is 0.12%,0.10%,0.11%and 0.21%respectively;the maximum estimated relative errors of parameters S1,S2,S3 and S4 are not more than 3%.The results are as follows:q=0.97,tanβ=1.98,b=0.39,θ=86.8°,S1=-5.07 m,S2=-17.84 m,S3=58.01 m,S4=36.38 m,the fitting error between subsidence and horizontal movement is 109.31 mm,which agrees with the engineering application standard.

关 键 词：概率积分法 开采深陷预测 混合蛙跳算法 参数反演 

分 类 号：TD325[矿业工程—矿井建设]