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投稿时间:2021-07-01 修订日期:2021-07-08
投稿时间:2021-07-01 修订日期:2021-07-08
中文摘要: 边坡稳定性受多种复杂因素影响,传统算法很难得到高精度预测结果,为了及时准确地对边坡稳定性做出可靠性分析,提出了改进粒子群优化极限学习机(IPSO-ELM)模型并应用于边坡稳定性预测实例中。首先在粒子群算法(PSO)的基础上,为克服在寻优过程中易出现局部最优的问题,引入自适应权重法,将改进粒子群算法(IPSO)对极限学习机(ELM)的输入权值和隐层偏置进行优化,大大提高了ELM模型的泛化能力和预测精度,然后将IPSO-ELM模型、PSO-ELM模型和ELM模型的预测值与真实值对比分析,结果表明IPSO-ELM模型预测值趋近于真实值,有较高的预测精度,验证了IPSO-ELM模型在评价边坡稳定性中的可行性和有效性。
中文关键词: 边坡稳定性 改进的粒子群算法(IPSO) 极限学习机(ELM) 自适应权重法 预测
Abstract:Slope stability is affected by a variety of complex factors, and it is difficult for traditional algorithms to obtain high precision prediction results. In order to timely and accurately analyze the reliability of slope stability, an improved particle swarm optimization extreme learning machine (IPSO-ELM) model was proposed and applied to the example of slope stability prediction.First in the basic particle swarm optimization (PSO), on the basis of which were liable to occur during the optimization process in order to overcome the local optimal problem, the introduction of the adaptive weight method, the improved particle swarm optimization (IPSO) for extreme learning machine (ELM) the weights of the input and hidden layer offset optimization, significantly improve the generalization ability and forecasting precision of the ELM model, and then will IPSO - ELM model, the PSO - ELM model and the predicted values and the real value of the ELM model comparison and analysis, the results show that IPSO - ELM model prediction approach in the real value, have higher prediction accuracy,The feasibility and effectiveness of IPSO-ELM model in slope stability evaluation are verified.
keywords: slope stability improved partical swarm optimization algorithm (IPSO) extreme learning machine(ELM) adaptive weighting method prediction
文章编号: 中图分类号:TD76 文献标志码:
基金项目:辽宁省教育厅重点项目(编号:601009877-36); 校青年(编号2020QN10)
作者 | 单位 | |
赵允坤 | 辽宁科技大学土木工程学院 | 1215901001@qq.com |
胡 军* | 辽宁科技大学土木工程学院 | |
杨 斌 | 辽宁科技大学土木工程学院 |
引用文本:
赵允坤,胡 军,杨 斌.基于IPSO-ELM的边坡稳定性分析[J].有色金属工程,2022,(1):.
赵允坤,胡 军,杨 斌.基于IPSO-ELM的边坡稳定性分析[J].有色金属工程,2022,(1):.