基于GOA-SVR的在线英语学习人数预测  

Forecast Number of Online English Students Based on GOA-SVR

作　　者：崔鹤[1] 薛媛[2] CUI He;XUE Yuan(Department of Basic Science, Shanxi Industrial Vocational and Technical College, Xianyang 712000;School of Fashion and Art Design, Xi'an Engineering University, Xi'an 710000)

机构地区：[1]陕西工业职业技术学院基础部,咸阳712000 [2]西安工程大学艺术设计学院,西安710000

出　　处：《微型电脑应用》2019年第5期128-131,共4页Microcomputer Applications

摘　　要：为了提高在线英语学习人数的预测精度,在保证预测值和实际值之间的误差最小化的前提下,针对SVR模型的预测精度受参数组合C、ε和g的值的选择影响,提出一种基于GOA-SVR的在线英语学习人数预测方法。选择我国2002～2017年在线英语学习人数为研究对象,以均方根误差和相关系数作为预测结果的评价指标,研究结果表明,GOA-SVR可以有效提高了在线英语学习人数预测精度。为在线英语学习人数预测提供了新的方法和途径,从而为在线英语学习资源管理和调度提供了重要的参考依据。In order to improve the prediction accuracy of the number of online English learners, under the condition that the error between the forecast value and the actual value is minimized, this paper proposes an online English learner number prediction method based on GOA-SVR to improve the prediction accuracy of the SVR model which is affected by the selection of the values of parameter combinations C,ε and g. The data of online English learners in China from 2002 to 2017 are selected as the research object, the mean square root error and correlation coefficient are used as the evaluation indicators, the calculating results show that GOA-SVR can effectively improve the accuracy of the online English learners forecast. This method provides a new method for forecasting the number of online English learners, and thus provides an important reference for the management and scheduling of online English learning resources.

关 键 词：蝗虫优化算法 支持向量机 遗传算法 粒子群算法 在线学习 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]