An artificial intelligence method for comprehensive evaluation of preschool education quality - Niu, Peilin (Frontiers in Psychology, 2022)
Evolution on quality of teaching for preschool education is very worth studying. In this article, we solve the qualitative problems in the comprehensive quality evaluation with the method of quantitative combination, and establish a set of indicators suitable for the comprehensive quality evaluation of students in the kindergarten of preschool education combination. According to the experience summed up by previous scholars, the weight of each index is obtained by analytic hierarchy process. This paper analyzes the defects and causes of fuzzy comprehensive evaluation and neural network model in the construction of early childhood and preschool education' comprehensive quality evaluation model, and discusses a FNN model. FNN combined with neural network and fuzzy logic characteristic, introducing fuzzy concepts and fuzzy inference rules into neural networks of neurons, the connection power and network learning, improve the learning ability of NN and fuzzy evaluation of the power of expression, effectively exert the advantages of fuzzy logic and neural network, make up for their shortcomings. But the convergence speed is very slow, in order to solve this problem, the similarity measure is used to improve the number of hidden layer nodes of the network, and the effectiveness and feasibility of the FNN improved hidden layer nodes are verified by an example, so as to realize the automation of comprehensive quality evaluation.