Teaching quality evaluation prediction method based on PSO optimized RBF model

Abstract

The invention discloses a teaching quality evaluation prediction method based on PSO optimized RBF model. A teacher teaching quality evaluation model based on a PSO optimized RBF neural network modelovercomes difficulty in a traditional teacher teaching quality evaluation process. Compared with other evaluation methods, the teaching quality evaluation prediction method has advantages of making anevaluation simulating result and precision accord with reality, effectively reducing influence of artificial factors in index weight determining, and promoting enthusiasm of teachers. The teaching quality evaluation prediction method based on the particle swarm optimization algorithm improved neural network model can dynamically optimize a central point position and a weight matrix of an RBF neural network model hidden layer, thereby improving performance of the RBF neural network, and improving data prediction accuracy. Furthermore a least square method is used for calculating a weight matrix, thereby reducing small number of required points and high convergence speed.

Description

technical field [0001] The invention belongs to the technical field of computer application engineering, and in particular relates to a prediction method based on a PSO-optimized RBF neural network model in the evaluation of teaching quality in higher vocational education. Background technique [0002] In recent years, in the process of mass education development, the scale of higher vocational colleges has grown rapidly. Teachers are relatively scarce, and the problem of teaching quality has become increasingly prominent. If vocational education ignores this problem, it will surely bring higher vocational education into a new predicament, and even affect the sustainable and healthy development of higher vocational education. Therefore, how to improve the quality of teaching has attracted more and more attention. To improve the teaching quality, it is necessary to improve the teacher teaching quality evaluation system. [0003] Since the teaching quality evaluation of tea...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to deal with nonlinear evaluation problems such as teaching quality evaluation, which can overcome the problems of lack of self-learning ability of traditional evaluation methods, and often have subjectivity when determining the weight of each evaluation index, etc., and give full play to artificial The superiority of the neural network is a new method for evaluating the quality of teachers' teaching, which can objectively and fairly evaluate the quality of teachers' teaching, mobilize teachers' enthusiasm for teaching, improve teaching quality, and cultivate outstanding talents

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