| 基于多元回归和BP神经网络的科技产出影响因素分析与预测研究 |
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| 引用本文： | 胡泽文,武夷山.基于多元回归和BP神经网络的科技产出影响因素分析与预测研究[J].科学学研究,2012,30(7):992-1004. |
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| 作者姓名： | 胡泽文 武夷山 |
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| 作者单位： | 1. 南京大学信息管理系2. 中国科学技术信息研究所 |
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| 基金项目： | 国家自然科学基金资助项目，江苏省普通高校研究生科研创新计划项目 |
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| 摘 要： | 笔者首先通过文献研究和网络调查等定性分析方法梳理出科技产出能力的所有可能的影响因素，并在数据可获得性的前提下，以1996-2008年为时间维，采集科技产出能力及其影响因素的相关数据，然后对科技产出能力及其影响因素之间的相互关系进行二元相关分析，并利用多元线性回归分析方法从所有相关因素中筛选出影响程度较高的因素，构建科技产出能力的影响因素分析与预测模型。最后基于二元相关分析的结果，选择相关程度较高的因素，利用目前流行的BP神经网络预测方法对科技产出能力进行预测研究，并与多元回归分析预测模型的预测性能进行比较。
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| 关 键 词： | 科技产出 影响因素分析 多元线性回归分析 BP神经网络 二元相关分析 预测 PCT专利申请 SCI论文产出 Scientific and technological outputs Impact factors analysis Multiple linear regression BP neural network Bivariate correlation analysis Prediction PCT patent applications SCI papers productivity |
| 收稿时间： | 2011-11-29; |
Research on analysis of influencing factors and prediction for scientific and technological outputs an approach based on multiple linear regression and BP neural network |
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| HU Ze-wen,WU Yi-shan.Research on analysis of influencing factors and prediction for scientific and technological outputs an approach based on multiple linear regression and BP neural network[J].Studies in Science of Science,2012,30(7):992-1004. |
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| Authors: | HU Ze-wen WU Yi-shan |
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| Institution: | 1.School of Information Management,Nanjing University,Jiangsu 210093,China; 2.Institute of Scientific & Technical Information of China,Beijing 100038,China) |
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| Abstract: | Firstly,some qualitative analysis methods such as literature research and network investigation are applied to find out all the possible factors influencing scientific and technological(S&T) outputs,and considering data availability,collect all related data to S&T productivity and their influencing factors for the period 1996-2008.Then based on the collected data,a bivariate correlation analysis method is utilized to analyse the mutual relations between S&T outputs and their influencing factors,and with the multiple linear regression method selecting the high-influencing factors to construct a model analyzing influencing factors and prediction for S&T outputs.Lastly based on the results of bivariate correlation analysis,a currently prevalent BP neural network prediction method is used to do a prediction study on S&T outputs,and compare the predictive performance with that of multiple linear regression method. |
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| Keywords: | multiple linear regression BP neural network scientific and technological outputs PCT patent applications SCI papers productivity impact factors analysis bivariate correlation analysis prediction |
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