scholarly journals Construction of Evaluation Model of University Student Education Evaluation System and Its MATLAB Simulation Based on BP Neural Network Algorithm

2018 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Evaluation Model ◽  
University Student ◽  
Education Evaluation ◽  
Matlab Simulation ◽  
Student Education ◽  
Simulation Based ◽  
Neural Network Algorithm

scholarly journals Credit of small and medium sized scientific and technological enterprises based on BP neural network Evaluation research

2021 ◽  
Vol 336 ◽  
pp. 09010
Author(s):  
Liulu Zhang ◽  
Xiao Zuo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Credit Rating ◽  
Evaluation Model ◽  
Science And Technology ◽  
Evaluation Research ◽  
Credit Evaluation ◽  
Neural Network Algorithm ◽  
Model Training

Aiming at the problem of credit evaluation of science and technology-based small and medium-sized enterprises in China, a credit evaluation system based on machine learning is proposed. A total of 17 indicators are selected from five aspects of solvency, profitability, operation ability, growth ability and R & D ability. Finally, 11 representative indicators are selected. Then through BP neural network algorithm to build a credit evaluation model, training and Simulation of the credit rating of science and technology-based SMEs. The results show that the evaluation model has good generalization ability, and can effectively evaluate the credit of science and technology-based SMEs.

scholarly journals An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sen Tian ◽  
Jianhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Back Propagation ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Operation Rate ◽  
Network Methods ◽  
Neural Network Algorithm

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

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.

Researches on the Network Security Evaluation Method Based on BP Neural Network

2014 ◽  
Vol 686 ◽  
pp. 470-473 ◽  
Author(s):  
Yi Bin Zhang ◽  
Ze Quan Yan
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Computer Network ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Security Evaluation ◽  
Computer Network Security ◽  
Neural Network Algorithm

This paper first describes the basic theory of BP neural network algorithm, defects and improved methods, establishes a computer network security evaluation index system, explores the computer network security evaluation method based on BP neural network, and has designed to build the evaluation model, and shows that the method is feasible through the MATLAB simulation experiments.

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 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.

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