Han Fanshen, Zhang Chenxi, Zhu Delong, Zhang Fengrui
Graduate School, Gachon University, Seongnam, South Korea.
School of Business, Gachon University, Seongnam, South Korea.
Front Psychol. 2022 Mar 24;13:785301. doi: 10.3389/fpsyg.2022.785301. eCollection 2022.
This study combines the discovery methods and training of innovative talents, China's requirements for improving talent training capabilities, and analyses the relationship between the number of professional enrollments in colleges and universities and the demand for skills in specific places. The research learns the characteristics and training models of innovative talents, deep learning (DL), neural networks, and related concepts of the seasonal difference Autoregressive Moving Average (ARMA) Model. These concepts are used to propose seasonal autoregressive integrated moving average back propagation (SARIMA-BP). Firstly, the SARIMA-BP artificially sets the weight parameter values and analyzes the model's convergence speed, superiority, and versatility. Then, particle swarm optimization (PSO) algorithm is used to pre-process the model and test its independence. The accuracy of the model is checked to ensure its proper performance. Secondly, the model analyzes and predicts the relationship between the number of professional enrollments of 10 colleges and universities in a specific place and the talent demand of local related enterprises. Moreover, the established model is optimized and tested by wavelet denoising. Independent testing is done to ensure the best possible performance of the model. Finally, the weight value will not significantly affect the model's versatility obtained by experiments. The prediction results of professional settings and corporate needs reveal that: there is a moderate correlation between professional locations and corporate needs; colleges and universities should train professional talents for local enterprises and eliminate the practical education concepts.
本研究结合创新人才的发现方法与培养，中国提高人才培养能力的要求，并分析高校专业招生人数与特定地区技能需求之间的关系。该研究了解创新人才、深度学习（DL）、神经网络以及季节性差分自回归移动平均（ARMA）模型相关概念的特点和培养模式。这些概念被用于提出季节性自回归积分移动平均反向传播（SARIMA-BP）。首先，SARIMA-BP人工设置权重参数值并分析模型的收敛速度、优越性和通用性。然后，使用粒子群优化（PSO）算法对模型进行预处理并测试其独立性。检查模型的准确性以确保其性能良好。其次，该模型分析并预测特定地区10所高校的专业招生人数与当地相关企业人才需求之间的关系。此外，通过小波去噪对建立的模型进行优化和测试。进行独立测试以确保模型的最佳性能。最后，实验表明权重值不会对模型的通用性产生显著影响。专业设置与企业需求的预测结果表明：专业设置与企业需求之间存在适度相关性；高校应为当地企业培养专业人才并消除实践教育观念。
