计算机工程

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修正型果蝇算法优化GRNN 网络的尾矿库安全预测

王英博1,聂娜娜1,王铭泽2,李仲学3   

  1. (1. 辽宁工程技术大学软件学院,辽宁葫芦岛125105; 2. 中央财经大学会计学院,北京100081;3. 北京科技大学金属矿山高效开采与安全教育部重点实验室,北京100083)
  • 收稿日期:2014-06-09 出版日期:2015-04-15 发布日期:2015-04-15
  • 作者简介:王英博(1964 - ),男,教授、博士,主研方向:数据挖掘;聂娜娜、王铭泽,硕士;李仲学,教授、博士生导师。
  • 基金项目:
    国家科技支撑计划基金资助项目(2013BAH12F00);中国煤炭工业科技计划基金资助项目(MTKJ2009-285)。

Mine Tailings Facilities Safety Evaluation of GRNN Optimized by Modified Fruit Fly Algorithm

WANG Yingbo 1,NIE Nana 1,WANG Mingze 2,LI Zhongxue 3   

  1. (1. College of Software,Liaoning Technical University,Huludao 125105,China;2. School of Accountancy,Central University of Finance and Economics,Beijing 100081,China; 3. Key Laboratory of Safety and Efficient Mining of Metal Mines,Ministry of Education,Beijing University of Science and Technology,Beijing 100083,China)
  • Received:2014-06-09 Online:2015-04-15 Published:2015-04-15

摘要: 针对尾矿库事故具有随机波动性和非线性的特点,提出采用修正型果蝇优化算法优化广义回归神经网络的尾矿库安全评价模型(MFOA-GRNN)。该方法利用修正型果蝇优化算法的全局寻优特性对广义回归神经网络进行参数优化,同时应用去相关性分析选取尾矿库安全评价指标,实现尾矿库的安全预测。以辽宁本溪南芬尾矿库为研究实例进行拟合预测,实验结果表明,将MFOA 方法与GRNN 网络有机结合,有利于平滑因子σ 的选择,相 较于FOA-GRNN 模型70% 的预测准确度,采用修正型果蝇算法优化的GRNN 模型预测准确度高达100% ,预测精度更高,适用性更强。

关键词: 尾矿库, 果蝇优化算法, 广义回归神经网络, 平滑因子, 参数优化, 安全预测

Abstract: At the mine tailings’ characteristics of stochastic fluctuation and nonlinear,and its safety prediction can be affected by many factors,a prediction model for mine tailings is put forward by adopting Modified fruit Fly Optimization Algorithm of the Generalized Regression Neural Network ( MFOA-GRNN ). The method introduces the global optimization characteristics of MFOA to optimize the parameter of GRNN,while using correlation analysis to select the mine tailings safety evaluation to achieve forecast. Taking Liaoning Benxi Nanfen mine tailing as research instance to fit forecast,it shows that combining MFOA with GRNN is beneficial to select the smoothing factor and compared with prediction accuracy 70% of the FOA-GRNN model,MFOA-GRNN model prediction accuracy is as high as 100% and has higher prediction precision and stronger applicability.

Key words: mine tailings facilities, Fly Optimization Algorithm ( FOA ), Generalized Regression Neural Network (GRNN), smoothing factor, parameter optimization, safety prediction

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