Teaching quality evaluation prediction method based on PSO optimized RBF model

A technology of teaching quality and prediction method, which is applied in the field of teaching quality evaluation in higher vocational education, can solve problems such as subjectivity and lack of self-learning ability, and achieve the effects of fast training speed, improved performance and strong stability

Inactive Publication Date: 2018-08-17
WUXI SOUTH OCEAN COLLEGE
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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 ind

Method used

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  • Teaching quality evaluation prediction method based on PSO optimized RBF model
  • Teaching quality evaluation prediction method based on PSO optimized RBF model
  • Teaching quality evaluation prediction method based on PSO optimized RBF model

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specific Embodiment

[0055] 1) Representation of data samples All the input data of the i-th sample are gathered together to form X i =(x i1 , x i2 ,...,x iN ), all output evaluation values ​​(expert evaluation values) of the i-th sample are collected together to form Y i =(y i1 ,y i2 ,...,y iM ).

[0056] 2) Data preprocessing

[0057] This patent inputs X to the i-th sample of the RBF network i =(x i1 , x i2 ,...,x iN ) and the output Y i =(y i1 ,y i2 ,...,y iM ) has been normalized, that is,

[0058]

[0059]

[0060] in, represent the maximum and minimum values ​​in the nth column input in all samples, respectively, respectively represent the maximum and minimum values ​​in the output of the mth column in all samples;

[0061] Input X' for the i-th sample after normalization i and output Y′ i , with x′ in ∈ [0, 1], y' im ∈ [0, 1]. Therefore, when training the RBF network with normalized samples, the data center c i The value range of each dimension in can be set...

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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...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/20
CPCG06Q10/04G06Q10/06393G06Q10/06395G06Q50/205
Inventor 廉颖霏
Owner WUXI SOUTH OCEAN COLLEGE
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