| Autor: |
Ren Rongrong, Fu Jia, Chao Jinboi, Yuan Huilin, Li Jing |
| Předmět: |
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| Zdroj: |
International Journal of Simulation: Systems, Science & Technology; 2016, Vol. 17 Issue 2, p3.1-3.7, 7p |
| Abstrakt: |
This paper evaluates students' comprehensive quality by using a number of indicators. The students' comprehensive quality indicators are nonlinearity which influence each other. The BP network model is good at solving nonlinear problem, so a BP neural network assessment model optimized by genetic algorithm has been established to judge the quality of students. The method uses historical data to train BP neural network. The results implemented by MATLAB show that, neural network possesses memorizing and learning capability, and its function can be achieved true judgment. Compared with BP neural network model, the model has the advantages of less number of iterations, convergence speed and strong generalization ability. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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