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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Tuesday 25 September 2018

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Taylor Approximation Method in Grey System Theory and Its Application to Predict the Number of Teachers and Students for Admission


Volume 10, Issue 2, October 2014, Pages 353–363

 Taylor Approximation Method in Grey System Theory and Its Application to Predict the Number of Teachers and Students for Admission

Phuoc-Hai Nguyen, Tian-Wei Sheu, Phung-Tuyen Nguyen, Duc-Hieu Pham, and Masatake Nagai

Original language: English

Received 8 August 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


The purpose of this study is to use T-GM(1,1) and T-GM(1,n) to predict the number of teachers and students for admission in Vietnam. T-GM(1,1) and T-GM(1,n) are two prediction models based on Taylor approximation method in grey system theory to improve the predicted accuracy of GM(1,1) and GM(1,n). Two combined models can obtain the most optimal values of prediction by multi-times approximate calculation. In addition, researchers used the MATLAB software to build a MATLAB toolbox for two prediction models. These results of this study not only are conducted to serve as a reference for the educational administrators but also can assist the government in developing future policies regarding educational management. This is critical for improving the overall quality of teaching and learning and improving the quality of education across the country.

Author Keywords: T-GM(1, 1), T-GM(1, n), Taylor approximation method, grey system theory, MATLAB toolbox.


How to Cite this Article


Phuoc-Hai Nguyen, Tian-Wei Sheu, Phung-Tuyen Nguyen, Duc-Hieu Pham, and Masatake Nagai, “Taylor Approximation Method in Grey System Theory and Its Application to Predict the Number of Teachers and Students for Admission,” International Journal of Innovation and Scientific Research, vol. 10, no. 2, pp. 353–363, October 2014.