基于RS-GWO-GRNN的充填管道失效风险研究

为克服充填管道失效风险评判指标间的复杂性,传统方法预测精度低及适用性差等缺陷,提出基于粗糙集(RS)和灰狼优化(GWO)算法融合广义回归神经网络(GRNN)的充填管道失效风险评价模型。选取10项风险评价指标,通过属性约简提取影响充填管道失效的主要风险因素,运用GWO优化GRNN的参数,构建预测模型,以国内某具体矿山充填系统为例进行实证研究,结果表明:与其它预测模型相比,RS-GWO-GRNN模型的预测精度更高,泛化能力更强,为充填管道失效风险研究提供了新思路,具有较好的借鉴意义。

Study on invalidation risk of backfill pipeline based on RS-GWO-GRNN

In order to overcome the complexity of evaluation index of failure risk for backfill pipeline and the defects of low precision and poor applicability of traditional methods, the paper presents a new method of backfill pipeline failure risk assessment model called GRNN fusion based on rough set (RS) and gray wolf optimization(GWO) algorithm. Ten risk evaluation indexes were selected, the main risk factors affecting filling pipeline failure were extracted through attribute reduction, and GWO was used to optimize the parameters of GRNN to build a forecasting model, taking a specific domestic mine filling system as an example for empirical research, The results show that compared with other prediction models, RS-GWO-GRNN model has higher prediction accuracy and stronger generalization ability, and provide a new idea for the research on the risk of backfill pipeline failure, which is of good reference significance.