Analysis on Quality Evaluation of Ecological Environment in Medium-sized and Small Towns based on BP Neural Network

2017 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Small Towns ◽  
Ecological Environment

scholarly journals Research of Students Learning Quality Evaluation System Based on BP Neural Network

2018 ◽  
Vol 1069 ◽  
pp. 012120
Author(s):  
Li Dai ◽  
Yuexiang Fan ◽  
Qi Lu ◽  
Mei Lin ◽  
Li Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Quality Evaluation System

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

The Model of Evaluating Teaching Quality Based on BP Neural Network Algorithm

2015 ◽  
Vol 719-720 ◽  
pp. 1297-1301
Author(s):  
Lei Bai ◽  
Xiao Xin Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Hot Spot ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Research Field ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Neural Network Algorithm

Teaching quality evaluation plays a key role for universities to improve its teaching quality and becomes a hot spot research field for related researchers. In this paper, we established the evaluation model of teaching quality based on BP neural network. Firstly an evaluation index system of teaching quality is designed. Then, according to the system we design the structure of BP neural network, determine the parameters and give the algorithm description. Finally, we program and verify the validity of the model in MATLAB environment. The experimental results show that the model can evaluate teaching quality practically by the evaluation index.

scholarly journals Cloud Education Chain and Education Quality Evaluation Based on Hybrid Quantum Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hong-Xia Liu ◽  
Yong-Heng Zhang ◽  
Sang-Bing Tsai
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
School Education ◽  
Bp Algorithm ◽  
Education Quality ◽  
Quantum Evolution ◽  
Application Service ◽  
Cloud Application ◽  
Evolution Algorithm

This paper proposes the functional model and application service implementation process of the education cloud platform application service architecture. The entire cloud application service architecture mainly includes four parts: cloud service management, cloud application service rapid creation and deployment, dynamic process configuration, and unified identity authentication. Based on the basic theory of workflow, the process status and business services of cloud application services are discussed. The BP neural network weight optimization model based on the improved quantum evolution method is studied, and a method that combines the improved quantum evolution algorithm (IQEA) and the BP algorithm to complete the back propagation neural network training is proposed, that is, the IQEA-BP algorithm. Firstly, the traditional quantum evolution algorithm is improved, and then, the improved quantum evolution algorithm is used to optimize the network weights as a whole to overcome the shortcomings of the BP algorithm that is easy to fall into the local optimum; then, we use the BP algorithm to find the better weight as the initial value to improve the training and prediction accuracy of the network. In order to enrich the school education quality evaluation system, this article adds soft indicators that can reflect school education performance on the basis of the existing “National Education Inspection Team” indicators and uses analytical methods to prove the effectiveness and feasibility of the new evaluation indicators. The X1-X10 index data is selected as the evaluation index of the school education quality evaluation system in this paper. Testing the performance of the BP neural network, the accuracy rate of the school education quality evaluation is 93.3%, the average absolute error is 0.067, and the accuracy and recall rate of the test set grade gradient of 0, 1, 2, 3, 5, 6, and 8 are all 93%, indicating that the IQEA-BP neural network algorithm has a good effect on the evaluation of school education quality.

The Application of Modified BP Neural Network Method to the Evaluation of the Quality of Teaching

2013 ◽  
Vol 710 ◽  
pp. 628-632
Author(s):  
Rong Song ◽  
Tao Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Calculation Error ◽  
Neural Network Method ◽  
Network Method ◽  
Calculation Results ◽  
Function Relationship ◽  
The Stability

Modified BP neural network method was used to solve the problem of teaching quality evaluation. The neural network was built to fit the function relationship between the second-floor indicator and teaching quality evaluation. So quality teaching evaluation could be implemented. At first, the theory of BP neural network method was introduced, then, students` evaluation of the secondary indicators was taken as inputs, and scores from the Steering Group as output, and 20 lessons scores as researched data, and then, calculating characters of BP method were analyzed. The calculating result showed that the calculation results of the method have the stability, its feasibility was proved. After that, the optimized calculating method was used to optimize result. The calculation results showed that the method had high accuracy, and predictive value calculation error was less than 2.02%, and it verified the feasibility of the method.

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