Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Yang, Qiangda; Fu, Yichuan; Zhang, Jie

doi:10.1007/s00521-020-05413-5

Cited by 6 publications

(1 citation statement)

References 49 publications

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“…Zhang et al combined the teaching characteristics of military schools and the requirements of the Ministry of Education for military school education and established a BP neural network-based military school teaching quality evaluation system to achieve a scientific, reasonable, and timely teaching quality evaluation of military school teaching quality [ 20 ]. Yang et al used the fuzzy comprehensive evaluation method to evaluate the teaching quality of university physics MOOC in three aspects, the degree of teaching goal achievement, the rationality of teaching content, and the appropriateness of teaching evaluation and feedback, and analyzed the problems of current university physics MOOC classroom teaching in response to the evaluation results [ 21 ]. Zhang et al tried to use the hierarchical analysis method, which was explored and analyzed, thus providing a more scientific example basis for evaluating bilingual course teaching [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network

Feng

2022

Computational Intelligence and Neuroscience

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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.

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Section: Related Workmentioning

confidence: 99%