基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型  

Fractional order grey model GSA-TSGM( 1,1) with time sequence variation regression driven by gravitational search algorithm

作　　者：高飞[1] 方海莲 Gao Fei;Fang Hailian(School of Sciences,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学理学院

出　　处：《计算机应用研究》2019年第6期1668-1672,共5页Application Research of Computers

基　　金：国家自然科学基金重大研究计划资助项目(91324201);湖北省自然科学基金资助项目(2014CFB865);国家教育部人文社科青年基金资助项目(14YJCZH143)

摘　　要：为了利用分数阶累加算子在灰色短期预测中的高效性能,首次将分数阶累加算子引入变异时序回归模型以期取得更高的预测精度。主要方法如下:首先取湖北省链子崖某监测点1978—1987年的十年数据作为训练集并使用引力搜索算法确定最佳分数阶累加阶数,而1988—1993年的六年数据作为验证集验证提出的模型;其次对比了经典灰色模型GM(1,1)、分数阶累加灰色模型、变异时序回归模型TSGM(1,1)三种灰色模型。结果如下:首先修正了陈西江等人变异时序回归模型仿真时出现的错误,其次表明了相比于其他的模型,基于引力搜索算法的分数阶累加时序回归模型在进行灰色长期预测中具有较高的预测精度。因此,通过分数阶累加算子提高了灰色理论中长期预测模型的精度,为灰色长期预测提供了指导。In order to use the high performance of fractional accumulated generating operator (FAGO) in the short-term grey prediction, this paper firstly added FAGO in the time sequence variation grey model TSGM(1,1) to get higher accuracy. The main method organized as follow. Firstly, it used the data of 1978 to 1987 from the monitoring station of “Lianziya” mountain in Hubei province as training data to optimal FAGO by using gravitational search algorithm and then used the data of 1988-1993 as verifying data to test the accuracy of the proposed grey model. Secondly, it compared other grey model GM(1,1), fractional accumulated generating GM(1,1) and TSGM(1,1). The result was that as follow. Firstly, it corrected the error in the simulation of time sequence variation regression model. Moreover, it shows that the proposed model has higher prediction accuracy. Therefore, the novel model improves accuracy of the grey theory in long-term prediction by fractional accumulated generating operator and it provides the guide in the long-term prediction.

关 键 词：分数阶累加算子 引力搜索算法 变异时序回归模型 灰色长期预测模型 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]