基于ANN-RBF的科技型中小微企业创新绩效分析与评价

Jincheng An, Xinlu Dong, Xintong Xie
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摘要

中小微科技企业的生存高度依赖创新,同时也具有较强的创新能力和发展潜力。本文在总结现有研究方法的基础上,采用一般、客观、可得的指标对创新和创新绩效进行量化。通过多次训练,建立平均总体相对误差为1.271的RBF (Radial Basis Function)神经网络,对所选样本数据进行分析。结果表明,对规范化重视度为100.0%的科研人员比例和对规范化合作取向的重视度为88.2%的科研人员比例显著影响创新绩效。本文的研究为中小微科技企业合理配置创新投入要素,有效提高企业创新绩效提供了科学的理论指导。
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Analysis and Evaluation of Innovation Performance of Micro, Small and Medium-sized Technology Enterprises Based on ANN-RBF
The survival of micro, small and medium-sized technology enterprises is highly dependent on innovation, while they also have strong innovation ability and development potential. Based on summarizing the existing research methods, this paper adopts the general, objective, and available indicators to quantify innovation and innovation performance. The RBF (Radial Basis Function) neural network is established with an average overall relative error of 1.271 through multiple pieces of training to analyze the selected sample data. The result shows that the proportion of researchers whose importance of normalization is 100.0 % and the cooperation orientation with normalization is 88.2 %, which significantly impacts innovation performance. The research of this paper provides scientific theoretical guidance for micro, small and medium-sized technology enterprises to rationally allocate innovation input factors and effectively improve their innovation performance.
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