摘要
最小二乘支持向量机预测时,其参数的选取大部分只依赖于人工经验,无法实现自适应寻优,阻碍了其学习与泛化能力。针对该问题,采用灰狼优化算法对最小二乘支持向量机参数寻优,以1978-2016全国红枣产量数据为研究对象,利用最小二乘支持向量机的最优参数对红枣产量数据进行拟合与预测。为避免过拟合现象,将1978-2007和2013-2016年数据分别作为模型的训练与预测数据,2008-2012年数据用于交叉验证,同时为检验该模型的预测性能,将其与ARIMA模型的预测效果进行对比分析。实证分析表明,基于灰狼优化算法的最小二乘支持向量机模型预测的平均相对误差小于ARIMA模型预测的平均相对误差,其可适用于红枣产量的预测,也进一步表明灰狼优化算法对最小二乘支持向量机参数优化的有效性。
When predicting least squares support vector machine,most of its parameters are only dependent on artificial experience,and adaptive optimization cannot be achieved,which hinders its learning and generalization ability.To solve this problem,we used the grey wolf optimization algorithm to optimize the parameters of the least squares support vector machine,and took the 1978-2016 national jujube production data as the research object,and used the optimal parameters of the least squares support vector machine to calculate the red jujube yield data.In order to avoid over-fitting,the data of 1978-2007 and 2013-2016 were used as the training and prediction data of the model,respectively.The data of 2008-2012 were used for cross-validation.At the same time,it was combined with ARIMA to test the predictive performance of the model.The prediction effect of the model was compared and analyzed.The empirical analysis showed that the average relative error of the least squares support vector machine model based on the grey wolf optimization algorithm was smaller than the average relative error predicted by the ARIMA model,which could be applied to the prediction of jujube yield,and further indicated that the grey wolf optimization algorithm was effective to the least square support vector machine parameter optimization.
作者
李鹏飞
王青青
毋建宏
樊怡彤
LI Peng-fei;WANG Qing-qing;WU Jian-hong(School of Economics and Management,Xi’an University of Posts and Telecommunications,Xi’an,Shaanxi 710061;School of Modern Posts,Xi’an University of Posts and Telecommunications,Xi’an,Shaanxi 710061)
出处
《安徽农业科学》
CAS
2020年第6期218-222,共5页
Journal of Anhui Agricultural Sciences
基金
国家社科基金(18FGL022)
教育部哲学社会科学研究后期资助项目(18JHQ082)
陕西省科技厅重大项目(2018ZDXM-GY-188)
陕西高校青年创新团队
陕西省教育厅服务地方专项项目(19JC037)
西安市科技计划项目(201806117YF05NC13(5))。
关键词
最小二乘支持向量机
全国红枣产量
灰狼优化算法
ARIMA
Least squares support vector machine
National jujube yield
Grey wolf optimization algorithm
ARIMA
