辽宁工程技术大学学报（自然科学版）2017，Vol.36Issue(5)：554-560,7.DOI:10.11956/j.issn.1008-0562.2017.05.020
IAPSO-LSSVM下的煤炭开采成本预测模型
Coal mining cost prediction model based on IAPSO-LSSVM
摘要
Abstract
In order to improve the prediction accuracy of least squares support vector machine (LSSVM) model,this paper used the global search ability of improved adaptive particle swarm optimization (IAPSO),searched the most optimal r and σ,and put forward a IAPSO-LSSVM prediction algorithm.According to the factors affecting the coal mining cost,spatial,temporal factors and qualitative factors,this study established the coal mining cost forecasting model based on IAPSO-LSSVM and carried out the simulation experiment with the data of TF coal mining group.The results show that the proposed model is better than the LSSVM and PSO-LSSVM method.关键词
煤炭开采成本/最小二乘支持向量机/粒子群算法/成本预测/模型改进Key words
coal mining cost/LSSVM/particle swarm optimization/prediction分类
管理科学引用本文复制引用
邰晓红,张慧嘉..IAPSO-LSSVM下的煤炭开采成本预测模型[J].辽宁工程技术大学学报（自然科学版）,2017,36(5):554-560,7.基金项目
国家科技支撑计划(2013BAH12F01) （2013BAH12F01）
