一种智能决策支持方法,通过改进的BP神经网络量化评估创新能力
Ming Chen1, Yan Qi1, Xinxing Zhang1
1Hebei Building Materials Vocational and Technical College, Qinhuangdao 066004, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
传统的方法无法评估大学生的创新和创业能力. 本研究提出了一种改进的反向传播 (BP) 神经网络模型,用于准确的智能评估,显示低误差率和强大的概括性.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 机器学习 机器学习
背景情况:
- 创新和创业能力对于大学生发展至关重要.
- 传统的评估方法不足以准确评估这些能力.
- 不一致的评估标准阻碍了客观评估.
研究的目的:
- 为大学生创新和创业能力开发一个全面而准确的评估模型.
- 提出使用机器学习的智能决策支持方法.
- 解决传统评估方法的局限性.
主要方法:
- 利用机器学习,特别是改进的反向传播 (BP) 神经网络.
- 优化的BP神经网络架构:输入,隐藏层和输出节点.
- 确定了转换函数,初始值,值和评估指标.
主要成果:
- 改进的BP神经网络模型显示出较低的错误率.
- 该模型表现出强大的概括能力.
- 该模型实现了评估创新能力的理想预测效应.
结论:
- 提议改进的BP神经网络模型为智能评估创新和创业能力提供了有效的解决方案.
- 与传统方法相比,这种方法提供了定量和更准确的评估.
- 该模型可用于分析教育评估中的复杂问题.
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