School of Marxism, Guangxi University of Chinese Medicine, Nanning 530200, China.
School of Public Health and Management, Guangxi University of Chinese Medicine, Nanning 530200, China.
Comput Intell Neurosci. 2022 Sep 30;2022:8437548. doi: 10.1155/2022/8437548. eCollection 2022.
Ideological and political education is the most important way to cultivate students' humanistic qualities, which can directly determine the development of other qualities. However, at present, the direction of ideological and political innovation in higher vocational colleges is vague. In response to this problem, this study proposes a model based on HS-EEMD-RNN. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the measured values, and then the recurrent neural network (RNN) is used to train each component and the remaining items. Finally, through the mapping relationship obtained by the model, the response prediction value of each component and the remaining items can be obtained. In the RNN training process, the harmony search (HS) algorithm is introduced to optimize it, and the noise is systematically denoised. Perturbation is used to obtain the optimal solution, thereby optimizing the weight and threshold of the RNN and improving the robustness of the model. The study found that, compared with EEMD-RNN, HS-EEMD-RNN has a better effect, because HS can effectively improve the training and fitting accuracy. The fitting accuracy of the HS-EEMD-RNN model after HS optimization is 0.9918. From this conclusion, the fitting accuracy of the HS-EEMD-RNN model is significantly higher than that of the EEMD-RNN model. In addition, four factors, career development, curriculum construction, community activities, and government support, have obvious influences on ideological and political classrooms in technical colleges. The use of recurrent neural networks in the research direction of deep and innovative research on the subject context of ideological and political classrooms can significantly improve the prediction accuracy of its development direction.
思想政治教育是培养学生人文素质的最重要途径，可以直接决定其他素质的发展。然而，目前高职院校思想政治创新的方向还比较模糊。针对这一问题，本研究提出了一种基于 HS-EEMD-RNN 的模型。首先，使用集合经验模态分解（EEMD）方法对测量值进行分解，然后使用递归神经网络（RNN）对每个分量和剩余项进行训练。最后，通过模型获得的映射关系，可以得到各分量和剩余项的响应预测值。在 RNN 训练过程中，引入了和声搜索（HS）算法对其进行优化，并对噪声进行系统去噪。通过扰动得到最优解，从而优化 RNN 的权重和阈值，提高模型的鲁棒性。研究发现，与 EEMD-RNN 相比，HS-EEMD-RNN 的效果更好，因为 HS 可以有效地提高训练和拟合精度。HS 优化后的 HS-EEMD-RNN 模型的拟合精度为 0.9918。由此得出结论，HS-EEMD-RNN 模型的拟合精度明显高于 EEMD-RNN 模型。此外，职业生涯发展、课程建设、社区活动和政府支持这四个因素对高职院校的思想政治课堂有明显影响。在思想政治课堂这一学科背景的深度学习和创新研究方向上，使用递归神经网络可以显著提高其发展方向的预测精度。
