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| Study on invalidation risk of backfill pipeline based on RS-GWO-GRNN |
| Received:March 04, 2018 Revised:April 01, 2018 |
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| DOI： |
| KeyWord:rough set(RS) theory; gray wolf optimization(GWO) algorithm; generalized regression neural network(GRNN); filling pipeline; failure risk |
| Author | Institution |
| LUO Zhengshan |
西安建筑科技大学管理学院 |
| WANG Wenhui |
西安建筑科技大学管理学院 |
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| Abstract: |
| 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. |
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