基于混沌-SVM-PSO的粮食产量预测方法研究
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摘要
针对粮食产量影响因素复杂、随机波动大等特点,对粮食产量预测问题展开研究。用混沌理论对原始样本进行相空间重构,确定最佳的嵌入维数和延迟时间。发挥粒子群算法全局搜索能力强的优点,用PSO算法优化SVM参数,避免人工选取参数的盲目性。以某省2004—2015年粮食产量预测为案例进行仿真试验,并将预测结果与灰色GM(1,1)模型进行对比。结果表明,本文所建模型对2014年、2015年粮食产量预测结果相对误差分别为-6.38%和2.07%,MAPE为4.22%,优于灰色GM(1,1)模型,具有较高的预测精度,从而验证所提方法的先进性和有效性。
Regarding on the characteristics of interaction factors'complexity and big fluctuation of food yield data,method for food yield prediction is studied.Phase space of original sample is reconstructed by chaotic Theory.Optimal delay time and embedding dimension are got.PSO has ability in global searching,so it is used to optimize parameters of SVM,which can avoid blindness of choosing parameters by predictor.Taking prediction of a province's grain yield from 2004 to 2015as an example.Prediction results are compared with GM(1,1).Simulation results show that relative error of yield food prediction for 2014 and 2015are-6.38% and2.07%.MAPEis 4.22%.Prediction accuracy of the method proposed above is higher than GM(1,1).So the advantage and validity of the method is verified.
Regarding on the characteristics of interaction factors'complexity and big fluctuation of food yield data,method for food yield prediction is studied.Phase space of original sample is reconstructed by chaotic Theory.Optimal delay time and embedding dimension are got.PSO has ability in global searching,so it is used to optimize parameters of SVM,which can avoid blindness of choosing parameters by predictor.Taking prediction of a province's grain yield from 2004 to 2015as an example.Prediction results are compared with GM(1,1).Simulation results show that relative error of yield food prediction for 2014 and 2015are-6.38% and2.07%.MAPEis 4.22%.Prediction accuracy of the method proposed above is higher than GM(1,1).So the advantage and validity of the method is verified.
引文
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[18]彭静,彭勇,欧阳令南.基于粒子算法和支持向量机的财务危机预警模型[J].上海交通大学学报,2008,42(4):615-619.
[2]张和喜,迟道才,王群,等.云推理模型在贵州省粮食产量预测中的应用[J].水土保持研究,2011,18(2):184-191.
[3]周永生,肖玉欢,黄润生.基于多元线性回归的广西粮食产量预测[J].南方农业学报,2011,42(9):1165-1167.
[4]陈生,李夫明.差分自回归移动平均模型在中国粮食产量预测中的应用[J].粮食科技与经济,2011,36(3):14-17.
[5]江和文,赵铭,郭婷婷,等.基于残差周期修正的辽宁省粮食产量灰色预测[J].气象与环境学报,2011,27(3):51-54.
[6]和文超,师学义,邓青云.土地利用规划修编中粮食产量预测方法比较[J].农业工程学报,2011,27(12):348-352.He Wenchao,Shi Xueyi,Deng Qingyun.Comparison of grain yield prediction methods in land use planning[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(12):348-352.
[7]丁铁山,郭冬冬,温季,等.基于人工神经网络的辽宁省粮食产量模型预测研究[J].安徽农业科学,2010,38(35):20429-20430,20440.
[8]张浩,王国伟,苑超,等.基于AIGA-BP神经网络的粮食产量预测研究[J].中国农机化学报,2016,37(6):205-209.Zhang Hao,Wang Guowei,Yuan Chao,et al.Research on forecast of grain production based on AIGA-BP neural network[J].Journal of Chinese Agricultural Mechanization,2016,37(6):205-209.
[9]王笑岩,王石.基于BP神经网络的辽宁省农机总动力预测[J].中国农机化学报,2015,36(2):314-317.Wang Xiaoyan,Wang Shi.Prediction on total power of agricultural machinery in Liaoning Province based on BP neural network[J].Journal of Chinese Agricultural Mechanization,2015,36(2):314-317.
[10]尤文坚,叶雪英,唐仕云.基于径向基神经网络农机数量预测的研究[J].中国农机化学报,2013,34(2):38-41.You Wenjian,Ye Xueying,Tang Shiyun.Research on forecast of the number of agricultural machinery based on RBF neural network[J].Journal of Chinese Agricultural Mechanization,2013,34(2):38-41.
[11]鲁敏,岑红蕾,王洪坤.基于LM-BP的新疆玛纳斯灌区用水量预测[J].中国农机化学报,2014,35(2):75-77.Lu Min,Cen Honglei,Wang Hongkun.Prediction of Xinjiang manas irrigation area water consumption based on LM-BP[J].Journal of Chinese Agricultural Mechanization,2014,35(2):75-77.
[12]王丽艳,郭树国.基于BP神经网络玉米蛋白粉吸水性的预测[J].中国农机化学报,2013,34(6):125-128.Wang Liyan,Guo Shuguo.Water absorption index prediction for corn gluten meal based on BP neural network[J].Journal of Chinese Agricultural Mechanization,2013,34(6):125-128.
[13]Vapnik V N.Statistical learning theory[M].John Wiley,New York,1998.
[14]罗利娟,丁宏飞.基于PSO-SVR的粮食产量预测模型[J].统计与决策,2010(22):36-38.
[15]张文金,许爱军.混沌理论和LSSVM相结合的网络流量预测[J].计算机工程与应用,2013,49(15):101-104,156.
[16]Kim H S,Eykholt R,Salas J D.Nonlinear dynamics,delay times,and embedding windows[J].Physica D,1999,127:48-60.
[17]康海贵,李明伟,周鹏飞,等.基于混沌高效遗传算法优化SVM的交通量预测[J].武汉理工大学学报(交通科学与工程版),2011,35(4):649-653.Kang Haigui,Li Mingwei,Zhou Pengfei,et al.Prediction of traffic flow using Support Vector Machine optimized by chaos higher efficient genetic algorithm[J].Journal of Wuhan University of Technology(Transportation Science&Engineering),2011,35(4):649-653.
[18]彭静,彭勇,欧阳令南.基于粒子算法和支持向量机的财务危机预警模型[J].上海交通大学学报,2008,42(4):615-619.
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