Chen Ming, Qi Yan, Zhang Xinxing, Jiang Xueyong
Hebei Building Materials Vocational and Technical College, Qinhuangdao 066004, China.
Yanshan University, Qinhuangdao 066004, China.
Math Biosci Eng. 2023 Jul 17;20(8):15120-15134. doi: 10.3934/mbe.2023677.
In today's competitive and changing social environment, innovation and entrepreneurial ability have become important factors for the successful development of college students. However, relying solely on traditional evaluation methods and indicators cannot comprehensively and accurately evaluate the innovation and entrepreneurial potential and ability of college students. Therefore, developing a comprehensive evaluation model is urgently needed. To address this issue, this article introduces machine learning methods to explore the learning ability of subjective evaluation processes and proposes an intelligent decision support method for quantitatively evaluating innovation capabilities using an improved BP (Back Propagation) neural network. This article first introduces the current research status of evaluating the innovation and entrepreneurship ability of college students, and based on previous research, it has been found that inconsistent evaluation standards are one of the important issues at present. Then, based on different BP models and combined with the actual situation of college student innovation and entrepreneurship evaluation, we selected an appropriate input layer setting for the BP neural network and improved the setting of the middle layer (hidden layer). The identification of output nodes was also optimized by combining the current situation. Subsequently, the conversion function, initial value and threshold were determined. Finally, evaluation indicators were determined and an improved BP model was established which was validated using examples. The research results indicate that the improved BP neural network model has a low error rate, strong generalization ability and ideal prediction effect which can be effectively used to analyze problems related to intelligent evaluation of innovation ability.
在当今竞争激烈且不断变化的社会环境中，创新和创业能力已成为大学生成功发展的重要因素。然而，仅依靠传统的评估方法和指标无法全面、准确地评估大学生的创新和创业潜力与能力。因此，迫切需要开发一种综合评估模型。为解决这一问题，本文引入机器学习方法来探索主观评估过程的学习能力，并提出一种使用改进的BP（反向传播）神经网络对创新能力进行定量评估的智能决策支持方法。本文首先介绍了大学生创新创业能力评估的当前研究现状，基于以往研究发现评估标准不一致是当前重要问题之一。然后，基于不同的BP模型并结合大学生创新创业评估的实际情况，为BP神经网络选择了合适的输入层设置，并改进了中间层（隐藏层）的设置。还结合现状对输出节点的识别进行了优化。随后，确定了转换函数、初始值和阈值。最后，确定了评估指标并建立了改进的BP模型，并通过实例进行了验证。研究结果表明，改进的BP神经网络模型错误率低、泛化能力强且预测效果理想，可有效用于分析与创新能力智能评估相关的问题。
