修正型果蝇算法优化GRNN网络的尾矿库安全预测

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

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。

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 forw ard by adopting M odified fruit Fly Optimization Algorithm of the Generalized Regression Neural Netw ork（ M FOA-GRNN）. The method introduces the global optimization characteristics of M FOA to optimize the parameter of GRNN,w hile 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 show s that combining M FOA w ith GRNN is beneficial to select the smoothing factor and compared w ith prediction accuracy 70% of the FOA-GRNN model,M FOA-GRNN model prediction accuracy is as high as 100% and has higher prediction precision and stronger applicability.