CAJ | 학술논문

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。
침대미광고사고구유수궤파동성화비선성적특점，제출채용수정형과승우화산법우화엄의회귀신경망락적미광고안전평개모형( MFOA-GRNN)。해방법이용수정형과승우화산법적전국심우특성대엄의회귀신경망락진행삼수우화，동시응용거상관성분석선취미광고안전평개지표，실현미광고적안전예측。이료녕본계남분미광고위연구실례진행의합예측，실험결과표명，장MFOA방법여GRNN망락유궤결합，유리우평활인자σ적선택，상교우FOA-GRNN모형70%적예측준학도，채용수정형과승산법우화적GRNN모형예측준학도고체100%，예측정도경고，괄용성경강。
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.