[1] 芦千文, 吕之望. 中国农机作业服务体系的形成、演变与影响研究[J]. 中国经济史研究, 2019(2): 124-135. LU Q W, LYU Z W. Research on formation, evolution, influence of agricultural machinery operation service system after 1949 in China[J]. Researches in Chinese Economic History, 2019(2): 124-135.(in Chinese with English abstract) [2] 柏明娜. 中国农业机械化服务与粮食生产研究[J]. 江西农业, 2018(4): 122. BAI M N.Research on agricultural mechanization service and grain production in China[J]. Jiangxi Agriculture, 2018(4): 122.(in Chinese) [3] 李建伟, 周洪, 赵汉雨, 等. 基于支持向量机的中国农业机械总动力预测[J]. 河南农业大学学报, 2013, 47(3): 296-300. LI J W, ZHOU H, ZHAO H Y, et al.Prediction on total power of Chinese agricultural machinery based on support vector machine[J]. Journal of Henan Agricultural University, 2013, 47(3): 296-300.(in Chinese with English abstract) [4] 周杰, 刘立波. 基于灰色BP神经网络的农业机械总动力预测[J]. 农机化研究, 2016, 38(9): 43-47. ZHOU J, LIU L B.Prediction of the total power of agricultural machinery based on grey BP neural network[J]. Journal of Agricultural Mechanization Research, 2016, 38(9): 43-47.(in Chinese with English abstract) [5] 张淑娟, 赵飞. 基于Shapley值的农机总动力组合预测方法[J]. 农业机械学报, 2008, 39(5): 60-64. ZHANG S J, ZHAO F.Combinatorial forecast of agricultural machinery total power based on Shapley value[J]. Transactions of the Chinese Society for Agricultural Machinery, 2008, 39(5): 60-64.(in Chinese with English abstract) [6] 王金峰, 闫东伟, 鞠金艳, 等. 基于经验模态分解与BP神经网络的农机总动力增长预测[J]. 农业工程学报, 2017, 33(10): 116-122. WANG J F, YAN D W, JU J Y, et al.Prediction of total power growth of agricultural machinery based on empirical mode decomposition and BP neural network[J]. Transactions of the CSAE, 2017, 33(10): 116-122.(in Chinese with English abstract) [7] 何志连, 王福林, 董慧英, 等. BP神经网络最佳停止法对农机总动力的预测[J]. 农机化研究, 2017, 39(2): 1-5. HE Z L, WANG F L, DONG H Y, et al.The application of the BP neural network with the best method of stop in forecast of total power of agriculture machinery[J]. Journal of Agricultural Mechanization Research, 2017, 39(2): 1-5.(in Chinese with English abstract) [8] 王吉权, 王福林, 邱立春. 基于BP神经网络的农机总动力预测[J]. 农业机械学报, 2011, 42(12): 121-126. WANG J Q, WANG F L, QIU L C.Prediction of total power in agriculture machinery based on BP neural network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(12): 121-126.(in Chinese with English abstract) [9] 艾洪福, 潘贺. BP网络在吉林省农机总动力预测中的应用[J]. 中国农机化学报, 2016, 37(8): 208-211. AI H F, PAN H.Application of BP network in prediction of agricultural machinery in Jilin Province[J]. Journal of Chinese Agricultural Mechanization, 2016, 37(8): 208-211.(in Chinese with English abstract) [10] 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 X Y, WANG S.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.(in Chinese with English abstract) [11] 谢建文, 张元标. 无偏灰色预测模型在农业机械总动力预测中的应用[J]. 安徽农业科学, 2008, 36(20): 8397-8398. XIE J W, ZHANG Y B.Application of unbiased grey model in total power requirement of agricultural machinery forecasting[J]. Journal of Anhui Agricultural Sciences, 2008, 36(20): 8397-8398.(in Chinese with English abstract) [12] 郑文钟, 应霞芳. 农业机械总动力变化影响因素的灰色关联分析[J]. 农机化研究, 2007, 29(12): 9-11. ZHENG W Z, YING X F.Correlative degree analysis between agricultural machinery gross power and influencing factors[J]. Journal of Agricultural Mechanization Research, 2007, 29(12): 9-11.(in Chinese with English abstract) [13] 许淑芹, 周桂霞, 许娇娜, 等. 混沌理论和灰色系统在农机总动力预测中的应用[J]. 数学的实践与认识, 2019, 49(3): 29-34. XU S Q, ZHOU G X, XU J N, et al.Application of chaotic theory and gray system to forecast agricultural machinery total power[J]. Mathematics in Practice and Theory, 2019, 49(3): 29-34.(in Chinese with English abstract) [14] 轩俊伟, 郑江华. 基于GWR的新疆农机总动力空间异质性分析[J]. 农机化研究, 2016, 38(5): 36-42. XUAN J W, ZHENG J H.Analyzing the spatial heterogeneity of agricultural machinery power in Xinjiang based on GWR[J]. Journal of Agricultural Mechanization Research, 2016, 38(5): 36-42.(in Chinese with English abstract) [15] 瞿明凯, 李卫东, 张传荣, 等. 地理加权回归及其在土壤和环境科学上的应用前景[J]. 土壤, 2014, 46(1): 15-22. QU M K, LI W D, ZHANG C R, et al.Geographically weighted regression and its application prospect in soil and environmental sciences[J]. Soils, 2014, 46(1): 15-22.(in Chinese with English abstract) [16] 赵旭. 基于指数平滑模型的农机总动力和综合机械化水平预测[J]. 农业装备与车辆工程, 2010, 48(2): 11-13. ZHAO X.The prediction of total power agricultural machinery and integrated mechanization level based on exponential smoothing model[J]. Agricultural Equipment & Vehicle Engineering, 2010, 48(2): 11-13.(in Chinese with English abstract) [17] 朱立学, 罗锡文, 臧英, 等. 基于3次指数平滑的水稻生产综合机械化发展水平预测[J]. 农机化研究, 2007, 29(7): 51-53. ZHU L X, LUO X W, ZANG Y, et al.3-exponential flatting method based on rice mechanization forecast[J]. Journal of Agricultural Mechanization Research, 2007, 29(7): 51-53. (in Chinese with English abstract) [18] 胡陈君, 陈建, 王卓, 等. 基于GA-LM-BP模型的云南省农机总动力预测[J]. 农机化研究, 2018, 40(4): 47-52. HU C J, CHEN J, WANG Z, et al.Prediction in the total power of Yunnan province's agricultural machinery based on GA-LM-BP mold[J]. Journal of Agricultural Mechanization Research, 2018, 40(4): 47-52.(in Chinese with English abstract) [19] 严磊, 毛凤梅, 雷邦军, 等. 农机总动力预测的灰色神经网络新方法[J]. 中国农机化学报, 2013, 34(3): 45-48. YAN L, MAO F M, LEI B J, et al.A new machinery total power forecasting method based on grey neural network[J]. Journal of Chinese Agricultural Mechanization, 2013, 34(3): 45-48.(in Chinese with English abstract) [20] 王丽伟. 新疆兵团农业机械总动力的组合预测模型研究[D]. 石河子: 石河子大学, 2018. WANG L W.The research on the combined forecast model of total power of agricultural machinery in Xinjiang corps[D]. Shihezi: Shihezi University, 2018.(in Chinese with English abstract) [21] 崔红艳. 吉林省农业机械总动力发展研究: 基于主成分分析[J]. 农机化研究, 2016, 38(6): 93-97. CUI H Y.Study on the development of total power of agricultural machinery in Jilin Province: based on principal component analysis[J]. Journal of Agricultural Mechanization Research, 2016, 38(6): 93-97.(in Chinese with English abstract) [22] 周浩, 李红. 基于面板数据模型的农机购置补贴政策分析[J]. 浙江农业学报, 2013, 25(6): 1449-1455. ZHOU H, LI H.Analysis on the allowance for purchasing agricultural machinery based on the panel data model[J]. Acta Agriculturae Zhejiangensis, 2013, 25(6): 1449-1455.(in Chinese with English abstract) [23] 鞠金艳, 赵林, 王金峰. 农机总动力增长波动影响因素分析[J]. 农业工程学报, 2016, 32(2): 84-89. JU J Y, ZHAO L, WANG J F.Fluctuations influence factors analysis of growth of agricultural machinery total power[J]. Transactions of the CSAE, 2016, 32(2): 84-89.(in Chinese with English abstract) [24] 吐尔逊·买买提, 丁为民, MUHAMMAD H. 基于灰色神经网络和MIV的农机总动力影响因素研究[J]. 中国农业资源与区划, 2017, 38(11): 24-30. TURSUN M, DING W M, MUHAMMAD H.Study on impact for agricultural machinery total power based on grey neural network and MIV[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2017, 38(11): 24-30. (in Chinese with English abstract) [25] 孙云鹏. 1978年以来中国农机总动力时空差异和结构变化分析[J]. 农机化研究, 2011, 33(5): 1-5. SUN Y P.Regional and timing disparity and structural change analysis of Chinese agricultural machinery total power since 1978[J]. Journal of Agricultural Mechanization Research, 2011, 33(5): 1-5.(in Chinese with English abstract) [26] 张燕, 徐华君. 中国农业机械动力空间格局分析[J]. 农机化研究, 2015, 37(11): 12-16. ZHANG Y, XU H J.Analyzing the spatial pattern of agricultural machinery power per hectare in China[J]. Journal of Agricultural Mechanization Research, 2015, 37(11): 12-16. (in Chinese with English abstract) |