基于随机权重法改进PSO-ELM的露天矿边坡稳定性分析

为提高极限学习机(ELM)模型在弓长岭露天矿边坡稳定性预测中的精度，有效解决ELM模型在训练过程中随机产生的连接权值和隐含层偏置而导致模型稳定性差的问题，引入基于随机权重法改进的粒子群算法(IPSO)进行优化，提出了改进粒子群算法优化极限学习机(IPSO-ELM)模型，将该模型应用到弓长岭露天矿边坡监测的数据中，把预测结果与ELM模型和PSO-ELM模型的预测值进行对比分析。结果表明：IPSO-ELM模型预测值接近于实测值，预测精度高、预测速度快、模型构建合理，在露天矿边坡预测中具有较高的可行性，可作为露天矿边坡预测的一种参考方法。

Slope stability analysis of open-pit mine based on improved PSO-ELM with random weight method

In order to improve the accuracy of extreme learning machine ( ELM ) model in the prediction of slope stability in Gongchangling Open-pit Mine and effectively solve the problem of poor stability caused by the random connection weights and hidden layer thresholds of ELM model in the training process, the improved particle swarm optimization ( IPSO ) based on random weight method is introduced to optimize, and the improved particle swarm optimization optimization extreme learning machine ( IPSO-ELM ) model is proposed. The model is applied to the monitoring data of Gongchangling Open-pit Mine slope. The prediction results are compared with the prediction values of ELM model and PSO-ELM model. The results show that the prediction value of IPSO-ELM model is close to the measured value, the prediction accuracy is high, the prediction speed is fast, and the model construction is reasonable. It has high feasibility in the prediction of open-pit slope and can be used as a reference method for open-pit slope prediction.