摘要
文章以长三角地区的27个城市为研究对象,构建了一个包含创新环境、创新资源和创新产出3个一级指标和10个二级指标的城市创新能力评价指标体系。运用基于人工神经网络的评价方法,以随机生成的数据作为训练集,搭建并训练了RBF神经网络模型,利用该模型对测试集进行评估,得到了城市创新能力的评分和排名。研究结果表明,RBF神经网络模型具有较高的准确性和稳定性,能够有效地反映城市创新能力的差异和水平,为城市创新能力的评价和提升提供了一种新的方法和参考。
This article proposes to construct an evaluation index system for urban innovation capacity,which consists of three primary indicators of innovation environment,innovation resources,and innovation output,and ten secondary indicators,with 27 cities in the Yangtze River Delta region as research objects.Then,by means of an evaluation method based on artificial neural networks,an RBF neural network model was constructed and trained with randomly generated data as the training set.Following that,the model was used to evaluate the test set and obtain the scores and rankings of urban innovation capability.The research findings suggest that the RBF neural network model exhibits high accuracy and stability,and can effectively reflect the differences and levels of urban innovation capabilities,thus serving as a new method for the evaluation o f urban innovation capabilities.
作者
冯赟
Feng Yun(School of Economics and Management,TongJi University,Shanghai,201804)
出处
《中阿科技论坛（中英文）》
2024年第2期22-26,共5页
China-Arab States Science and Technology Forum
关键词
城市创新能力评价
评价指标体系
RBF人工神经网络
Evaluation of urban innovation capability
Evaluation index system
RBF artificial neural network
