Employment Guidance Teaching and Research Department, Henan Institute of Technology, Xinxiang 453000, Henan, China.
Comput Intell Neurosci. 2022 Aug 2;2022:8272445. doi: 10.1155/2022/8272445. eCollection 2022.
Entrepreneurship education activities in colleges and universities play an important role in improving students' innovation ability. Therefore, this paper has important practical value to evaluate the innovation and entrepreneurship ability of college students. At present, most studies use qualitative research methods, which is inefficient. Even if quantitative analysis is adopted, it is mostly linear analysis, which is inconsistent with the actual situation. In order to improve the application level of genetic algorithm to the innovation and entrepreneurship ability of universities based on BP neural network, this paper studies the evaluation model of innovation and entrepreneurship ability of universities. Based on the simple analysis of the current situation of university innovation and entrepreneurship ability evaluation and the application progress of BP neural network, combined with the actual situation of university innovation and entrepreneurship, this paper constructs the innovation and entrepreneurship evaluation index, uses BP neural network to build the evaluation model, and uses genetic algorithm to optimize and improve the shortcomings of BP neural network. Then, the experimental analysis and application design are carried out. The results show that the improved algorithm is basically consistent with the predicted value, small error, and fast convergence. When it is used in the evaluation of innovation and entrepreneurship ability, quantitative analysis results can be obtained, which provides a certain reference for the development of enterprises.
高校创业教育活动对提高学生的创新能力起着重要作用。因此，本文对大学生创新创业能力的评价具有重要的现实意义。目前，大多数研究采用定性研究方法，效率低下。即使采用定量分析，也大多是线性分析，与实际情况不符。为了提高遗传算法在基于 BP 神经网络的高校创新创业能力中的应用水平，本文研究了高校创新创业能力的评价模型。本文在对高校创新创业能力评价现状及 BP 神经网络应用进展进行简单分析的基础上，结合高校创新创业实际，构建了创新创业评价指标，利用 BP 神经网络构建评价模型，并利用遗传算法对 BP 神经网络的不足进行优化改进。然后进行了实验分析和应用设计。结果表明，改进后的算法与预测值基本一致，误差较小，收敛速度较快。将其应用于创新创业能力评价中，可以得到定量分析结果，为企业发展提供了一定的参考。
