A Fuzzy Neural Network-Based System for Alleviating Students’ Boredom in English Learning

Abstract

In order to explore students’ boredom in English learning, a recognition algorithm based on fuzzy neural network is proposed. The algorithm selects Gaussian membership function and initializes the clustering center obtained by fuzzy c-means algorithm to the center of Gaussian function, and the width of Gaussian function is obtained by the membership and center of fuzzy c-means clustering algorithm. In the construction of base classifier, diversity strategy is adopted to increase its diversity and complementarity. In the selection of base classifiers, the combination of contour coefficient and clustering algorithm is used to determine the number of classifiers to be fused, and the inconsistency measurement method is used to evaluate their differences. In the combination strategy, we learn from the Bayesian thought and dynamically adapt the weight of learning base classifier based on its a priori probability and class conditional probability. The experimental results show that 10 times of cross-validation are carried out, respectively, and the accuracy of each algorithm is given. The algorithm based on tree structure obviously has better performance, followed by the rule-based algorithm, and finally the fuzzy neural network algorithm based on neural network, while the accuracy of SVM and logistic regression algorithm LR is lower. It is proved that the fuzzy neural network can effectively identify students’ boredom in English learning.