Directors of Comprehensive Quality System of College Students in China: Based on BP Neural Network

Qian ZHANG, Ling WANG, Ying MA

Abstract


Chinese college students’ comprehensive quality is evaluated by using a number of indicators. The students’ comprehensive quality indicators are nonlinearity and uncertainty which can not be measured accurately. The BP network model can be a good solution to this problem. For this purpose, the BP neural network integrated assessment model for students has been established to effectively judge the quality of all aspects of the university students, and evaluate the reliability of the analysis. Research on the BP network model, its function can be achieved true judgment, made out of the heavy information inquiry, summarizing work out.

Keywords


BP neural network; Comprehensive evaluation; Evaluation results

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References


Beldiman, O., Walsh, G. C., & Bushnell, L. (2004). Predictors for networked control systems. Proceeding of IEEE International Conference on Control Applications, 1, 242-247.

Chen, X. Y., Guan, Y., & Chen, C. (2008). Research on multi-parameter fault pattern recognition of electro-hydraulic servo value based on BP neural networks. Proceedings of the 7th World Congress on Intelligent Control and Automation, 6049-6052.

Hu, N. T., & Li, L. (2009). An improve BP neural network model based on quasic-newton algorithm. Proceedings of the 5th International Conference on Natural Computation, 2, 352-356.

Jiang, W. L., Li, Q. P., & Li, T. (2003). The problems and countermeasures of Neural Network Research. Machine Tool and Hyraulics, (5), 29-32.

Li, S., Wang, Z., & Sun, Y. (2004). Observer-based compensator design for network control systems with long time delays. Proceeding of the 30th Annual Conference of IEEE Industrial Electronics Society, 1, 678-683.

Li, X. Y., Qi, B., & Wu, L. (2009). A new improve BP neural network algorithm. Proceedings of the 2nd International Conference on Interlligent Computation Technology and Automaton, i, 19-22.

Rosenblatt, F. (1958). The brain psychological eviewbol. The brain (pp.386-408).

Rovithakis, G. A., Gaganis, V. I., & Perrakis, S. E. (1999). Real-time control of manufacturing cells using dynamic neural networks. Automatic, 135(l), 139-149.

Wang, Q. Y. (1985). Principles and methods of artificial intelligence. University of Xi’an Transpertation Express (references), 529-551.

Xiu, R., Wang, X. M., & Li. Y. (2010). Research and application on improved BP neural network algorithm. Proceedings of the 5th IEEE Conference on Industrial Electronics and Applications, 1462-1466.




DOI: http://dx.doi.org/10.3968/j.mse.1913035X20130703.2623

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