School of Marxism, Dalian Ocean University, Dalian, Liaoning 116023, China.
School of Marine Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong 519000, China.
Comput Intell Neurosci. 2022 Jun 11;2022:7893792. doi: 10.1155/2022/7893792. eCollection 2022.
We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.
本文提出了一种模糊 BP 神经网络模型和 DDAE-SVR 深度神经网络模型来分析多模态数字化教学，建立了基于模糊 BP 神经网络的多模态数字化教学质量数据评价模型，并利用自适应变异遗传算法优化 BP 神经网络的初始权值和阈值。由于 BP 神经网络对初始权值和阈值高度依赖，因此采用改进的遗传算法对 BP 神经网络的初始权值和阈值进行优化，减少了 BP 神经网络寻找满足训练终止条件的重要性和点的时间，提高了神经网络对教学质量评价结果的预测精度和收敛速度。引入基于数据的客观评价方法——熵值法作为 BP 神经网络的指导机制。通过熵方法获得先验指导样本，然后采用自适应变异遗传算法对 BP 神经网络模型进行优化，学习先验样本知识，建立评价模型，减少 BP 神经网络学习样本的主观性。为了更好地反映和比较两种神经网络评价模型（BP 和 GA-BP）的效果，将样本数据继续输入到原始 GA 和 BSA 中，得到评价结果和误差；然后，将两种评价模型（BP 和 GA-BP）的评价结果与两种算法（GA 和 BSA）的评价结果进行比较。结果发现，GA-BP 神经网络评价模型具有更高的准确性，可用于多模态数字化教学质量评价，为其提供了更可行的解决方案。
