摘要
由于监测环境恶劣,变形监测序列常伴有较大波动,针对灰色模型(gray model,GM)仅适用于分析指数型变形序列,且最小二乘支持向量机(least squares support vector machine,LSSVM)在进行变形预测时存在参数难以有效选取的问题,提出了一种改进的灰色最小二乘支持向量机变形预测模型(IGM-LSSVM)。将几何平均生成变换引入GM(1,1)模型,增强其输入样本的指数规律性,初步预测出变形值并计算残差;针对人工蜂群算法(artificial bee colnony,ABC)在优化LSSVM参数时易陷入局部极值的缺陷,引入Metropolis准则并为其设计了自适应降温函数,得到自适应Metropolis人工蜂群算法(adaptive metropolis artificial bee colnony,AMABC);利用AMABC算法优化的LSSVM训练GM(1,1)模型得到的预测残差值补偿GM(1,1)模型,得到最终预测值。某矿区边坡变形预测表明:AMABC算法有效克服了ABC算法易陷入局部最优解的缺点,IGM-LSSVM、GM(1,1)、ABC-GM-LSSVM等模型预测的平均相对误差分别为1.223%,9.565%、3.200%,可见,IGM-LSSVM的预测精度相对于其余2种模型优势明显,对于实现矿区边坡变形高精度预测有一定的参考价值。
Due to the poor monitoring environment,deformation monitoring sequence often accompanied with large fluctuations.The grey model(GM)is only suitable for solving exponential deformation series,and the least squares support vector machine(LSSVM)is difficult to effectively select the parameters in the deformation prediction.In order to sovle the existing problems of GM and LSSVM,an improved grey least squares support vector machines deformation prediction model(IGMLSSVM)is proposed.Firstly,the geometric mean generating transformation is introduced into GM(1,1)model to enhance the exponential regularity of its input samples,and the deformation values are initially predicted,the residuals are also calculated;secondly,according to the disadvantages that artificial bee colony algorithm(ABC)is easily fall into local extremum when optimizing the parameters of LSSVM,the metropolis criterion is introduced and adaptive cooling function is designed to get an adaptive metropolis colony algorithm(AMABC);finally,a set of residual values that predicted by LSSVM based on AMABC algorithm is used to compensate the GM(1,1)model,and the final prediction value is obtained.The deformation prediction results of a mining area show that the shortcomings that the ABC algorithm is easy to fall into local optimal solution is solved effectively,the average relative error of IGM-LSSVM,G(1,1)and ABC-GM-LSSVM are 1.223%,9.565% and 3.200%,the prediction precise of IGM-LSSVM is higher than other two models,which further show that IGM-LSSVM is suitable for the the large fluctuation deformation monitoring sequence,and it has certain reference for realizing high precision deformation prediction of mine slope.
作者
冯腾飞
刘小生
钟钰
马玉清
Feng Tengfei;Liu Xiaosheng;Zhong Yu;Ma Yuqing(School of Architectural and Surveying & Mapping Engineering,Jiangxi University of Science and Technology,Gtinzhou 341000,China)
出处
《金属矿山》
CAS
北大核心
2019年第3期168-172,共5页
Metal Mine
基金
国家自然科学基金项目(编号:41561091)
