基于GEP的金属矿尾矿坝变形预测模型研究  

Deformation prediction model of metal mine tailings dam based on GEP

作　　者：刘传立[1] 刘小生[1] 李妍妍[1] 

机构地区：[1]江西理工大学建筑与测绘工程学院,江西赣州341000

出　　处：《有色金属科学与工程》2013年第6期63-68,共6页Nonferrous Metals Science and Engineering

基　　金：国家自然科学基金资助项目(41261093);江西省教育厅科技研究项目资助(GJJ10488)

摘　　要：尾矿坝的变形监测是金属矿山企业生产管理极其重要的环节,针对目前尾矿坝变形预测模型存在不足的现状,论文采用了基因表达式编程(GEP)算法,以Eclipse为开发工具,通过选择函数集和终止符集、种群初始化、染色体解码、适应度评估、遗传操作等过程,建立了基于GEP-Deep Excavation的尾矿坝变形预测模型,并对某金属矿山尾矿坝监测点位移数据进行了预测;经与灰色GM(1,1)和BP神经网络2种模型试验对比分析,证实了基于GEP的尾矿坝变形预测模型的可行性和有效性,从而为金属矿山尾矿坝的变形预测提供了一种新方法.Deformation monitoring of tailings dam is a very important part for metallic mine enterprises in production management. In view of the existing defects of railings dam deformation prediction model, this paper has established the prediction model of tailings dam deformation based on GEP-Deep Excavation, and made a prediction for observation displacement in a certain metal mine tailings dam by GEP （Gene Programming） algorithm with Eclipse as terminating character sets, population i a development tool, through the process of selecting a set operation. By contrastive analysis of the gr zation, ay GM（ the chromosome decoding, fitness evaluation and BP neural network, empirical studies Expression of functions, and genetic confirm the feasibility and effectiveness of the prediction model of tailings dam deformation based on GEP. This paper provides a new method for tailings dam deformation prediction of metal mine.

关 键 词：尾矿坝 GEP 灰色模型 BP神经网络 变形预测 

分 类 号：TD76[矿业工程—矿井通风与安全] TP183[自动化与计算机技术—控制理论与控制工程]