摘要
In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.
In view of the difficulty in supporting the surrounding rocks of roadway 3-411 of Fucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was put forward based on particle swarm optimization. The kernel function and model parameters were optimized using particle swarm optimization. It is shown that the forecast result is very close to the real monitoring data. Furthermore, the PSO-SVM （Particle Swarm Opti- mization-Support Vector Machine） model is compared with the GM（1,1） model and L-M BP network model. The results show that PSO-SVM method is better in the aspect of predic- tion accuracy and the PSO-SVM roadway deformation pre-diction model is feasible for the large deformation prediction of coal mine roadway.
基金
Supported by the National Natural Science Foundation of Zhejiang Province(2009C33049)
the National Natural Science Foundation of China(50674040)
