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Mathematical Problems in Engineering
Volume 2014, Article ID 586284, 7 pages
Research Article

An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China

1School of Economics & International Trade, Zhejiang University of Finance & Economics, Hangzhou 310018, China
2School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China

Received 17 September 2013; Revised 12 January 2014; Accepted 19 January 2014; Published 24 February 2014

Academic Editor: Jitao Sun

Copyright © 2014 Zheng-Xin Wang and Ling-Ling Pei. 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.


The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and series related, abbreviated as GDMC, performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC, interpolation coefficients (taken as unknown parameters) are introduced into the background values of the variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC model. The modelling results can assist the government in developing future policies regarding high-tech industry management.