基于SAFA优化LSSVM的粮食产量预测
|
摘要
为提高粮食产量预测的精度,针对LSSVM模型的预测精度受惩罚参数C和核函数参数g选择的影响,将非线性惯性权重引入萤火虫算法,提出一种基于自适应权重的萤火虫算法(Self-Adaptive Firefly Algorithm,SAFA),并将SAFA应用于惩罚参数C和核函数参数g优化,提出一种基于SAFA-LSSVM的粮食产量预测算法。选择1978—2017年我国粮食产量数据为研究对象,与FA-LSSVM、PSO-LSSVM和LSSVM相比,研究结果表明本文提出的算法SAFA-LSSVM可以有效提高粮食产量预测的精度,相关系数R达0.9893,为粮食产量预测提供新的方法和途径。
In order to improve the precision of grain yield prediction, the nonlinear inertial weight is introduced into the Firefly algorithm for the influence of selecting the parameter C and the kernel function parameter g of the LSSVM model, and an adaptive weight based Firefly algorithm is proposed was applied to optimize the penalty parameter C and the kernel function parameter g. A grain yield prediction algorithm based on SAFA-LSSVM was proposed. China's grain yield data from 1978 to 2017 were selected as the research object, Compared with FA-LSSVM, PSO-LSSVM and LSSVM, the results show that the proposed algorithm SAFA-LSSVM can effectively improve the accuracy of grain yield prediction, R was 0.989 3, and provide new methods and approaches for grain yield prediction.
In order to improve the precision of grain yield prediction, the nonlinear inertial weight is introduced into the Firefly algorithm for the influence of selecting the parameter C and the kernel function parameter g of the LSSVM model, and an adaptive weight based Firefly algorithm is proposed was applied to optimize the penalty parameter C and the kernel function parameter g. A grain yield prediction algorithm based on SAFA-LSSVM was proposed. China's grain yield data from 1978 to 2017 were selected as the research object, Compared with FA-LSSVM, PSO-LSSVM and LSSVM, the results show that the proposed algorithm SAFA-LSSVM can effectively improve the accuracy of grain yield prediction, R was 0.989 3, and provide new methods and approaches for grain yield prediction.
引文
[1] 孙东升, 梁仕莹. 我国粮食产量预测的时间序列模型与应用研究[J]. 农业技术经济, 2010(3): 97-106.Sun Dongsheng, Liang Shiying. Research on China's grain output forecast time series model and application [J]. Journal of Agrotechnical Economics, 2010(3): 97-106.
[2] 韩书成, 李丹, 熊建华, 等. 广州市耕地资源数量变化及其对粮食安全的影响[J]. 农林经济管理学报, 2016, 15(6): 648-654.Han Shucheng, Li Dan, Xiong Jianhua, et al. Changes in cultivated land amount and their impacts on food security in Guangzhou [J]. Journal of Agro-Forestry Economics and Management, 2016, 15(6): 648-654.
[3] 朱迎春, 周志刚, 王大鹏. 江苏省农机化水平与影响因素灰色关联分析[J]. 中国农机化学报, 2009, 30(4): 12-14.Zhu Yingchun, Zhou Zhigang, Wang Dapeng. Correlative degree analysis between the level of agricultural mechanization and influencing factors in Jiangsu Province [J]. Journal of Chinese Agricultural Mechanization, 2009, 30(4): 12-14.
[4] Fei S W, Miao Y B, Liu C L. Chinese grain production forecasting method based on particle swarm optimization-based support vector machine [J]. Recent Patents on Engineering, 2009, 3(1): 8-12.
[5] 张宇青, 易中懿, 周应恒. 一种线性ARIMA基础上的非线性BP神经网络修正组合方法在粮食产量预测中的运用[J]. 数学的实践与认识, 2013, 43(22): 135-142.Zhang Yuqing, Yi Zhongyi, Zhou Yingheng. Forecast of grain output using a method of nonlinear BP neural network based on linear ARIMA modified [J]. Mathematics in Practice and Theory, 2013, 43(22): 135-142.
[6] 苏博, 刘鲁, 杨方廷. GM(1,N)灰色系统与BP神经网络方法的粮食产量预测比较研究[J]. 中国农业大学学报, 2006, 11(4): 99-104.Su Bo, Liu Lu, Yang Fangting. Comparison and research of grain production forecasting with methods of GM(1,N) gray system and BPNN [J]. Journal of China Agricultural University, 2006, 11(4): 99-104.
[7] 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): 37-38.Luo Lijuan, Ding Hongfei. Grain production forecasting model based on PSO-SVR [J]. Statistics and Decision, 2010(22): 37-38.
[8] Suykens J A K, Gestel T V, Brabanter J D, et al. Least squares support vector machines [J]. International Journal of Circuit Theory & Applications, 2002, 27(6): 605-615.
[9] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999, 9(3): 293-300.
[10] Baesens B, Viaene S, Gestel T V, et al. Least squares support vector machine classifiers: an empirical evaluation [J]. Access & Download Statistics, 2000: 1-16.
[11] 冯艳红, 刘建芹, 贺毅朝. 基于混沌理论的动态种群萤火虫算法[J]. 计算机应用, 2013, 33(3): 796-799.Feng Yanhong, LiuJianqin, He Yichao. Chaos-based dynamic population firefly algorithm [J]. Journal of Computer Applications, 2013, 33(3): 796-799.
[12] 刘佳昆, 周永权. 一种最大最小萤光素值人工萤火虫算法[J]. 计算机应用研究, 2011, 28(10): 3662-3664.Liu Jiakun, Zhou Yongquan. Glowworm swarm optimization algorithm based on max-min luciferin [J]. Application Research of Computers, 2011, 28(10): 3662-3664.
[13] 秦嵩, 赵道致, 王震蕾. 影响粮食产量因素的组合预测和经济分析[J]. 中国农机化学报, 2009, 30(2): 58-61.Qin Song, Zhao Daozhi, Wang Zhenlei. Economical analysis on factors related to the output of rice [J]. Journal of Chinese Agricultural Mechanization, 2009, 30(2): 58-61.
[14] 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): 22-27.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): 22-27.
[15] 潘永明, 米冠旭. 基于主成分分析法的天津现代物流发展综合评价模型构建[J]. 中国农机化学报, 2009, 30(4): 45-47.Pan Yongming, Mi Guanxu. Model of the modern logistics developmental of Tianjin integrated evaluation based on PCA [J]. Journal of Chinese Agricultural Mechanization, 2009, 30(4): 45-47.
[16] Yuefeng Y, Xuerong X. Principal component analysis and GM(1,1) forecast of grain output influencing factors in Fujian Province [J]. Journal of Southern Agriculture, 2014, 45(4): 697-703.
[17] 魏津瑜, 陈锐, 刘曰波. 影响我国粮食产量的因素分析及对策研究[J]. 中国农机化学报, 2008, 29(5): 56-59.Wei Jinyu, Chen Rui, Liu Yuebo. Analysis of effecting factors on China's grain yield and countermeasure [J]. Journal of Chinese Agricultural Mechanization, 2008, 29(5): 56-59.
[2] 韩书成, 李丹, 熊建华, 等. 广州市耕地资源数量变化及其对粮食安全的影响[J]. 农林经济管理学报, 2016, 15(6): 648-654.Han Shucheng, Li Dan, Xiong Jianhua, et al. Changes in cultivated land amount and their impacts on food security in Guangzhou [J]. Journal of Agro-Forestry Economics and Management, 2016, 15(6): 648-654.
[3] 朱迎春, 周志刚, 王大鹏. 江苏省农机化水平与影响因素灰色关联分析[J]. 中国农机化学报, 2009, 30(4): 12-14.Zhu Yingchun, Zhou Zhigang, Wang Dapeng. Correlative degree analysis between the level of agricultural mechanization and influencing factors in Jiangsu Province [J]. Journal of Chinese Agricultural Mechanization, 2009, 30(4): 12-14.
[4] Fei S W, Miao Y B, Liu C L. Chinese grain production forecasting method based on particle swarm optimization-based support vector machine [J]. Recent Patents on Engineering, 2009, 3(1): 8-12.
[5] 张宇青, 易中懿, 周应恒. 一种线性ARIMA基础上的非线性BP神经网络修正组合方法在粮食产量预测中的运用[J]. 数学的实践与认识, 2013, 43(22): 135-142.Zhang Yuqing, Yi Zhongyi, Zhou Yingheng. Forecast of grain output using a method of nonlinear BP neural network based on linear ARIMA modified [J]. Mathematics in Practice and Theory, 2013, 43(22): 135-142.
[6] 苏博, 刘鲁, 杨方廷. GM(1,N)灰色系统与BP神经网络方法的粮食产量预测比较研究[J]. 中国农业大学学报, 2006, 11(4): 99-104.Su Bo, Liu Lu, Yang Fangting. Comparison and research of grain production forecasting with methods of GM(1,N) gray system and BPNN [J]. Journal of China Agricultural University, 2006, 11(4): 99-104.
[7] 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): 37-38.Luo Lijuan, Ding Hongfei. Grain production forecasting model based on PSO-SVR [J]. Statistics and Decision, 2010(22): 37-38.
[8] Suykens J A K, Gestel T V, Brabanter J D, et al. Least squares support vector machines [J]. International Journal of Circuit Theory & Applications, 2002, 27(6): 605-615.
[9] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999, 9(3): 293-300.
[10] Baesens B, Viaene S, Gestel T V, et al. Least squares support vector machine classifiers: an empirical evaluation [J]. Access & Download Statistics, 2000: 1-16.
[11] 冯艳红, 刘建芹, 贺毅朝. 基于混沌理论的动态种群萤火虫算法[J]. 计算机应用, 2013, 33(3): 796-799.Feng Yanhong, LiuJianqin, He Yichao. Chaos-based dynamic population firefly algorithm [J]. Journal of Computer Applications, 2013, 33(3): 796-799.
[12] 刘佳昆, 周永权. 一种最大最小萤光素值人工萤火虫算法[J]. 计算机应用研究, 2011, 28(10): 3662-3664.Liu Jiakun, Zhou Yongquan. Glowworm swarm optimization algorithm based on max-min luciferin [J]. Application Research of Computers, 2011, 28(10): 3662-3664.
[13] 秦嵩, 赵道致, 王震蕾. 影响粮食产量因素的组合预测和经济分析[J]. 中国农机化学报, 2009, 30(2): 58-61.Qin Song, Zhao Daozhi, Wang Zhenlei. Economical analysis on factors related to the output of rice [J]. Journal of Chinese Agricultural Mechanization, 2009, 30(2): 58-61.
[14] 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): 22-27.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): 22-27.
[15] 潘永明, 米冠旭. 基于主成分分析法的天津现代物流发展综合评价模型构建[J]. 中国农机化学报, 2009, 30(4): 45-47.Pan Yongming, Mi Guanxu. Model of the modern logistics developmental of Tianjin integrated evaluation based on PCA [J]. Journal of Chinese Agricultural Mechanization, 2009, 30(4): 45-47.
[16] Yuefeng Y, Xuerong X. Principal component analysis and GM(1,1) forecast of grain output influencing factors in Fujian Province [J]. Journal of Southern Agriculture, 2014, 45(4): 697-703.
[17] 魏津瑜, 陈锐, 刘曰波. 影响我国粮食产量的因素分析及对策研究[J]. 中国农机化学报, 2008, 29(5): 56-59.Wei Jinyu, Chen Rui, Liu Yuebo. Analysis of effecting factors on China's grain yield and countermeasure [J]. Journal of Chinese Agricultural Mechanization, 2008, 29(5): 56-59.