检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
机构地区:[1]宁夏师范学院数学与计算机科学学院,宁夏固原756000
出 处:《兰州理工大学学报》2013年第3期65-69,共5页Journal of Lanzhou University of Technology
基 金:宁夏自然科学基金(NZ12228);宁夏高等学校科学研究项目(NJ201279;NJ201233681);宁夏师范学院创新团队资助项目(ZY201212);宁夏师范学院重点项目(ZD201311)的资助
摘 要:为了克服神经网络存在的收敛速度慢、容易陷入局部极值等缺点,提出基于粒子群优化支持向量机(PSO-SVM)的黄金价格预测方法,以影响黄金价格的美元走势、世界黄金储备、石油价格等因素为输入,黄金价格为输出.用粒子群优化算法选择合适的支持向量机参数,对支持向量回归机进行训练.应用训练完成的支持向量回归机预测下一年的黄金价格.结果证明,PSO-SVM的预测精度高于BP神经网络,PSO-SVM适用于黄金价格预测.In order to overcome the defect of neural network such as slow convergence rate and tendency to fall into local minimum, a gold price forecasting method was presented that based on p~ ticle swarm op- timization with support vector machine (PSO-SVM), where the dollar trend, world gold reserves, oil price and other factors that have influence on gold price were taken as input, and the gold price was taken as output. The particle swarm optimization algorithm was employed to select the appropriate parameters of support vector machine and the support vector regressive machine was trained. Then, the trained sup- port vector regressive machine was used to forecast the gold price in next year. The result showed that the forecasting accuracy with PSO-SVM was higher than that with BP neural network and PSO-SVM was ap- plicable for gold price forecast.
关 键 词:粒子群算法 支持向量机回归 黄金价格 参数优化 统计学习理论
分 类 号:TP183[自动化与计算机技术—控制理论与控制工程]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.229