scholarly journals Based on Optimization Research on the Evaluation System of English Teaching Quality Based on GA-BPNN Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Yafei Chen ◽  
Zhenbang Yu ◽  
Weihong Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
English Teaching ◽  
Algorithm Evaluation ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quality Evaluation System

English teaching is an important part of basic teaching in our country, which has been deeply concerned by all aspects. Its teaching quality not only is related to the purpose of English teaching, but also has a far-reaching impact on students’ English learning. Therefore, the construction of English teaching quality evaluation system has become the focus of research. However, the traditional English teaching quality evaluation method has some problems; for example, the subjectivity of teaching evaluation is strong, the evaluation index is not comprehensive, and the evaluation results are distorted. Therefore, this paper studies the English teaching quality evaluation system based on optimized GA-BP neural network algorithm. On the basis of BP neural network algorithm evaluation simulation, GA algorithm is introduced for optimizing, and GA-BP neural network algorithm model is further optimized by GA adaptive degree variation and entropy method. The experimental results show that the optimized GA-BP neural network algorithm has faster convergence speed and smaller error. At the same time, the optimized GA-BP neural network algorithm evaluation model has better adaptability and stability, and its expected results are more in line with the ideal value. The results of English teaching quality evaluation are more scientific, showing higher value in the application of English teaching quality evaluation.

scholarly journals Japanese Translation Quality Evaluation System Based on Deep Neural Network Algorithm

2022 ◽  
Vol 16 ◽  
pp. 433-439
Author(s):  
Rui Zhang
Keyword(s):  
Neural Network ◽  
Evaluation System ◽  
Deep Neural Network ◽  
Quality Evaluation ◽  
Processing Efficiency ◽  
Translation Quality ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quality Evaluation System ◽  
Japanese Translation

The current translation quality evaluation system relies on the combination of manual and text comparison for evaluation, which has the defects of low efficiency and large evaluation errors. In order to optimize the defects of the current quality evaluation system, a Japanese translation quality evaluation system based on deep neural network algorithm will be designed. In order to improve the processing efficiency of the system, the USB3.0 communication module of the hardware system will be optimized. Based on the hardware design, the reference translation map is used to extend the reference translation of Japanese translation. The evaluation indexes of over- and under-translation are set, and the evaluation of Japanese translation quality is realized after the parameters are determined by training the deep neural network using the sample set. The system functional test results show that the average data transmission processing time of the system is improved by about 31.27%, and the evaluation error interval is smaller and the evaluation is more reliable.

scholarly journals Optimization of Online Education and Teaching Evaluation System Based on GA-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Xu ◽  
Fenghu Liu
Keyword(s):  
Neural Network ◽  
Online Education ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Evaluation System ◽  
Teaching Quality ◽  
Teaching Evaluation ◽  
Classroom Teaching ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the popularization and application of online education in the world, how to evaluate and analyze the classroom teaching effect through scientific methods has become one of the important teaching tasks in colleges. Based on this, this paper studies the application of the GA-BP neural network algorithm. Firstly, it gives a brief overview of the current situation of online education and GA-BP neural network algorithm. Secondly, through the investigation of the online education system in many aspects, it evaluates students’ online education classroom teaching quality from five aspects, and this paper proposes a more scientific online education classroom teaching quality evaluation optimization model and finally verifies the reliability of the online education teaching evaluation model through the practice in a university. The results show that the GA-BP neural network-based evaluation optimization model can effectively evaluate the online education in the process of analyzing the quality of online education classroom teaching of most professional students.

scholarly journals College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yajun Chen
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Rbf Neural Network ◽  
Teaching Quality ◽  
Decision Algorithm ◽  
College English ◽  
English Teaching ◽  
Quality Evaluation System

In the process of deepening and developing the current higher education reform, people pay more and more attention to the research of college English education. The key to improve the college English education is to improve the quality of education, and learning evaluation is the key measure to improve the quality of education and training. This paper mainly studies the college English teaching quality evaluation system based on information fusion and optimized RBF neural network decision algorithm. This paper analyzes the main problems and complexity of creating an ideal learning quality evaluation system. On the basis of analyzing the advantages and disadvantages of the previous learning quality evaluation methods, this paper summarizes the existing learning quality evaluation methods and puts forward some suggestions according to the existing evaluation methods. A learning quality evaluation model based on RBF algorithm of neural network is proposed. RBF regularization network method, RBF neural network decision algorithm, and experimental investigation method are used to study the college English teaching quality evaluation system based on information fusion and optimization of RBF neural network decision algorithm. By innovating teaching methods and enriching teaching means, college students’ thirst for English knowledge can be aroused, and teachers’ teaching level can be improved. The results show that 50% of college students think that the level of college English teaching is average and needs to be improved. In the performance evaluation system of college English teaching quality based on information fusion and optimized RBF neural network decision algorithm, it is necessary to establish a learning evaluation system, monitor the learning quality in real time, find problems and improve them in time, and recognize the current situation of education.

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

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.

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