黄金科学技术 ›› 2021, Vol. 29 ›› Issue (2): 245-255.doi: 10.11872/j.issn.1005-2518.2021.02.072
• 采选技术与矿山管理 • 上一篇
Zhengshan LUO(),Renhui HUANG(),Guochen SHEN
摘要:
为克服传统预测模型存在的适用性差、预测精度不足和参数选取随意性强等缺陷,提出了一种将核主成分分析法(KPCA)、改进粒子群算法(IPSO)与最小二乘支持向量机(LSSVM)相结合的充填管道磨损风险预测新方法。通过KPCA对管道磨损影响因素进行特征提取,将提取结果作为LSSVM的输入,同时利用具有较强全局搜索能力的IPSO算法对模型参数进行优化,构建KPCA-IPSO-LSSVM组合预测模型。以黄陵县矿区的80组实测数据为例,对该模型进行训练和预测,并将其预测结果与IPSO-LSSVM模型、LSSVM模型、SVM模型的预测结果进行对比分析。结果表明:与其他3个预测模型相比,KPCA-IPSO-LSSVM模型具有较高的预测精度和较强的泛化能力,为充填管道磨损风险预测提供了一种更为有效的预测方法。
中图分类号:
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