| 摘要: |
| 石油需求量预测对编制石油产业发展规划具有重要意义。为了合理预测中国石油需求量,将1965—2014年中国国内生产总值、人口数量、产业结构及技术进步4个分量作为输入向量,石油需求量数据作为输出向量,建立中国石油需求预测的BP神经网络模型,利用Matlab软件的神经网络工具箱对BP神经网络模型反复训练,发现当隐含层节点数为17、学习率为0.1、训练次数为8次、训练精度为0.001时得到的效果最好。最后运用所确定的BP神经网络模型对2015—2024年中国石油需求数据进行了预测。 |
| 关键词: BP神经网络 石油需求 预测 |
| DOI：10.13216/j.cnki.upcjess.2015.03.0002 |
| 分类号:F407.22 |
| 基金项目:国家社会科学基金项目(12BJY075);中央高校基本科研业务费专项资金资助项目(13CX05044B) |
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| Research on Prediction of China 's Oil Demand Based on the BP Neural Network Model |
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LI Hongxun1, LI Yuanqing2, WANG Haijun3
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(1.School of Economics and Management, China University of Petroleum, Qingdao, Shandong 266580, China;2.CNOOC Tianjin Branch, Tangguo, Tianjin 300452, China;3.School of Management, Shanghai Jiaotong University, Shanghai 200030, China)
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| Abstract: |
| The prediction of oil demand has great significance in preparing the oil industry development planning. In order to reasonably predict China 's oil demand, take GDP, population, industrial structure and the technical progress as input vector and take the oil demand as the output vector, we establish the BP neural network model. After training of the BP neural network model by Matlab software, we find that when the number of hidden layer nodes for vector is 17, learning rate is 0.1, training times are 8 and training precision is 0.001, and the predicting result is the best. Finally, we use the BP neural network to predict China 's oil demand from 2015 to 2024. |
| Key words: BP neural network oil demand prediction. |
