中文摘要：

为提高农机总动力增长变化预测结果的准确性和可靠性,根据农机总动力增长变化与其影响因素之间具有在各时间尺度明显的非线性波动特征,提出以1986—2013年农机总动力增长为研究对象,分别对农机总动力增长及其影响因素时间序列数据进行经验模态分解（empirical mode decomposition,EMD）,对得到的各时间尺度下的波动分量分别建立BP神经网络预测模型。将EMD-BP网络预测结果与多元线性回归、支持向量机、BP神经网络进行对比分析,结果表明：基于EMD-BP网络建立的农机总动力增长预测模型,拟合和预测平均相对误差分别为0.99%和1.29%,相关决定系数约为0.999,均方根误差为316.35 MW,模型评价等级为＂好＂,各项精度评价指标都优于其他方法,因此该预测模型精度高、可靠性强。研究成果为农业机械化发展规划的制定和出台相关政策提供有效参考。

英文摘要：

The traditional time series prediction models and multi-factor linear regression prediction models for total power of agricultural machinery are difficult to meet the actual analysis and forecasting demand. The total power growth of agricultural machinery and its influencing factors have strong correlation and obvious nonlinear fluctuation characteristics in various time scales. Taking the time series data of the total power growth of agricultural machinery and its influencing factors from 1986 to 2013 as the research objects, the prediction model for the total power growth of agricultural machinery was proposed to improve the accuracy and reliability of prediction results based on empirical mode decomposition （EMD） and BP （back propagation） neural network. The total power growth of agricultural machinery was affected by many factors such as government macro policy, farmers＇ income growth, production scale expanding, production capacity improving, and so on. In order to determine the main influencing factors, the principal component analysis method was adopted to analyze the main contribution factors, and then the correlation analysis method was used to analyze the correlations between factors. The less affected factors were eliminated, and ultimately, planting area per labor, government finance investment, per capita net income of farmers, fuel price index and the number of first industry practitioners were determined as the main influencing factors, which were used to forecast the total power growth of agricultural machinery. The EMD method was adopted to decompose the total power growth of agricultural machinery and its main influencing factors from 1986 to 2013 in multi-time scale, the intrinsic mode functions （IMFs） with different time scales and the trend items were obtained, and then the nonlinear relationships between each IMF component and trend item of the total power growth of agricultural machinery and volatile component of influencing factors were established using BP network. At last,