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基于PCA和GA-BPNN的技术初创企业的评估和选

Jiaxin Li¹, Mingming Meng¹, Xin Liu¹

¹School of Economics & Management, Beijing Forestry University, Beijing, P.R. China.

|February 8, 2024

概括

风险投资机构现在可以更有效地选技术初创企业 (TSs),使用一种新的评估系统. 该系统结合了基因算法优化的逆向传播神经网络 (GA-BPNN),显著提高了识别合格投资的准确性.

科学领域:

风险投资和金融技术
金融领域的人工智能
创新管理创新管理

背景情况:

风险资本的信息不透明导致不利的选择和市场效率低下.
风险投资机构在精确选技术初创企业时面临挑战.
准确的TS评估对于高效的投资定位至关重要.

研究的目的:

为风险投资机构设计一个先进的评估和选系统.
加强对合格的技术初创企业 (TSs) 的选择过程.
减轻不利选择和提高风险资本市场效率.

主要方法:

开发了一个四维的研究框架:概念,技术创新,商业模式和团队结构.
使用文献研究提取了15个二级和33个三级指标.
为评估提出了一种基因算法优化的反向传播神经网络 (GA-BPNN).

主要成果:

GA-BPNN模型实现了高准确率:合格企业的准确率为80.33%,不合格企业的准确率为93.67%.
该模型在评估和选TS的有效性经验验证.
该系统显示了提高投资选择准确性的巨大潜力.

结论:

使用PCA和GA-BPNN建立了一个有效的评估系统,用于TSs的初步查.
该系统为风险投资机构提供了减少选时间和成本的机会.
该系统使科学风险评估成为可能,提高了TS投资的效率和利能力.

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