Based on Neural Network of University Teaching Laboratory Performance Management Evaluation Model Research

2013 ◽  
Vol 5 (7) ◽  
pp. 1029-1036
Author(s):  
Fang Chengxin
Keyword(s):  
Neural Network ◽  
Performance Management ◽  
Evaluation Model ◽  
University Teaching ◽  
Laboratory Performance ◽  
Teaching Laboratory ◽  
Management Evaluation ◽  
Model Research

scholarly journals An Improved Genetic Algorithm and Neural Network-Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Huaying Zhang ◽  
Bin Xiao ◽  
Jinqiong Li ◽  
Min Hou
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
University Teaching ◽  
Classroom Teaching ◽  
Adaptive Mutation ◽  
Improved Genetic Algorithm

Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm.

An optimized BP neural network model for teaching management evaluation

2020 ◽  
pp. 1-7
Author(s):  
Xinhua Yang ◽  
JiaJia Zhou ◽  
DaoQun Wen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Optimal Solution ◽  
University Teaching ◽  
Swarm Optimization ◽  
Teaching Management ◽  
Management Evaluation ◽  
Neural Network Algorithm

To improve the effectiveness and intelligence of university teaching management evaluation, the particle swarm optimization BP neural network algorithm is applied to the analysis of university teaching management evaluation data. BP neural network is used to model the evaluation index of teaching management, and then particle swarm optimization is used to optimize the weight and threshold of the neural network transfer function to ensure that the output of the BP neural network can obtain the global optimal solution. The experimental results show that the proposed algorithm has a good fit between the predicted value and the actual value of the evaluation object of teaching management in Colleges and universities, and has a strong promotion value.

Fuzzy Comprehensive Evaluation of Human Performance Management

2013 ◽  
Vol 756-759 ◽  
pp. 715-719
Author(s):  
Huan Cheng Zhang ◽  
Ya Feng Yang ◽  
Feng Li ◽  
Li Nan Shi
Keyword(s):  
Performance Management ◽  
Evaluation System ◽  
Human Performance ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Process ◽  
College Performance ◽  
Performance Evaluation System ◽  
Comprehensive Evaluation Model

In the College, performance evaluation system is directly related to the harmonious development of the school. Taking into account the factors in the evaluation system is fuzzy, so this paper uses fuzzy comprehensive evaluation model. But the model is too subjective, so this paper combines neural network and data envelopment analysis method, which ensures that fuzzy comprehensive evaluation model is reasonable and scientific, and good school development and teacher self-interest. The performance assessment process, not only enables the combination of qualitative and quantitative analysis, but also fair and reasonably reflect the achievements of teachers, while this method is easy to use, wide application, and can be well applied in practice.

Study on Intelligent Analysis of the Causes of SMT Solder Joint Defects Based on Fuzzy Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 3257-3261
Author(s):  
Chun Yue Huang ◽  
He Geng Wei ◽  
Tian Ming Li ◽  
De Jin Yan
Keyword(s):  
Neural Network ◽  
Solder Joint ◽  
Membership Function ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Training Samples ◽  
Intelligent Analysis ◽  
Fuzzy Neural ◽  
Input Variables ◽  
Set Up

By determining membership function of the input parameters and selecting defuzzification method, the evaluation model which can be used to intelligent analyzing the causes of SMT solder joint defects was set up. The fuzzy neural network was trained by using the output variables of the training samples from intelligent discrimination as the input variables of training samples of fuzzy neural network. The fuzzy neural network was tested by using the output variables of the testing samples from intelligent discrimination as the input variables of testing samples of fuzzy neural network. The results show that by using the evaluation model the cause of SMT solder joint defects can be analyzed intelligently and the results of intelligently analysis are reasonable, the evaluation model can be used practically.

Application of T-S Fuzzy Neural Network in the Assessment of River Ecosystem Health

2013 ◽  
Vol 726-731 ◽  
pp. 958-962 ◽  
Author(s):  
Zhen Chun Hao ◽  
Xiao Li Liu ◽  
Qin Ju
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy System ◽  
Ecosystem Health ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Fuzzy Model ◽  
River Ecosystem ◽  
Fuzzy Neural

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.

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