Study on Optimization of Neural Network Based on Whale Algorithm for Prediction of Vault Subsidence in Slope
In order to more accurately predict the trend of vault subsidence in underground mine ramps and control the prediction accuracy to ensure mine safety,this paper proposes a whale optimization algorithm(WOA)enhanced neural network method for vault subsidence prediction.The main steps are as follows:the original data is processed using the method of adjacent point median smoothing firstly,and the processed monitoring data is used as input samples for training and testing BP and Elman neural networks;then the WOA is used to optimize the initial weights and thresholds,and finally different model output predictions are obtained.Simulation experiments show that the BP and Elman neural network models optimized by the whale optimization algorithm can more accurately predict the vault subsidence compared to before optimization.The determination coefficient of the WOA-Elman model is 0.948,which is superior to the WOA-BP model at 0.941,but the running time of the WOA-Elman model,671.214 s,far exceeds that of the WOA-BP model,307.226 s.The WOA-Elman model consumes more training time for a small improvement in accuracy,significantly reducing training efficiency.Combined with the analysis and comparison of measured values and predicted values from engineering examples,the WOA-optimized BP neural network exhibits more efficient and accurate vault subsidence prediction capability.
NETL
NSTL
万方数据
维普
