摘要
本研究联合应用BP神经网络和免疫遗传算法(BPANN-IGA)对2008—2015年全国科技资源配置效率进行分析,得到最优的科技资源投入产出组合策略,理论上最优科技资源配置效率可达到0.63。把最优组合策略与全国科技资源配置效率最高的北京市进行比较分析,发现高的科技资源配置效率要求科技人员精英化、科研机构精简化、经费投入精准化、资源分配市场化和产出更具有创新性的产品。
In this study, the BP artificial neural network and immune genetic algorithm（BPANN-IGA） are combined to analyze the efficiency of allocating scientific and technological resources from the 2008 to 2015 in China. The optimal input and output combination strategy of scientific and technological resources is calculated, and the optimal allocation efficiency of scientific and technological resources can reach 6.3 theoretically. The optimal strategy was also compared with the strategy in Beijing, where there is the highest allocation efficiency in China. It is found that the higher allocation efficiency requires the elite scientific researchers, the streamlining scientific research institutions, the accurate investment in scientific research, the marketing of resources distribution, and the more innovative products.
作者
彭建升
PENG Jian-sheng(Dept.of Science and Technology,Putian University,Putian 351100,Fujian,China)
出处
《集宁师范学院学报》
2018年第3期16-19,共4页
Journal of Jining Normal University
基金
福建省自然科学基金项目(编号:2017J05115)
创新创业生态系统评价体系构建与应用研究
莆田市科技计划项目(编号:2017AHX27)
莆田市科技发展报告
关键词
科技资源
配置效率
BP神经网络
遗传算法
Science and technology resources
allocation efficiency
BP artificial neural network
genetic algorithm
