基于PSO-BP的胶结充填体强度预测研究

Study on Strength Prediction of Cemented Backfill Based on PSO-BP

为快速、准确地确定胶结充填体强度,构建了基于PSO-BP的胶结充填体强度预测模型,并以养护7 d和28 d的胶结充填体强度试验数据进行了验证。结果表明:结合粒子群算法优化BP神经网络初始权值,从而大大提高了预测模型的准确性和可靠性,基于粒子群算法优化下的神经网络相对误差为0.77%,比BP神经网络预测的平均相对误差降低了3.42%,表现出良好的预测精度。

In order to quickly and accurately determine the strength of cemented backfill,a strength prediction model of cemented backfill based on PSO-BP was constructed and verified with the strength test data of cemented backfill at 7 d and 28 d of curing.The results show that the initial weights of BP neural network are optimized by combining particle swarm optimization algorithm,which greatly improves the accuracy and reliability of the prediction model.The relative error of neural network optimized by particle swarm optimization algorithm is 0.77%,which is 3.42%lower than the average relative error of BP neural network prediction,showing good prediction accuracy.