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

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  • Zheng-Xin Wang
  • Ling-Ling Pei

Abstract

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.

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  • Zheng-Xin Wang & Ling-Ling Pei, 2014. "An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, February.
    • Handle: RePEc:hin:jnlmpe:586284
    DOI: 10.1155/2014/586284
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    Cited by:

    1. Hang Jiang & Peiyi Kong & Yi-Chung Hu & Peng Jiang, 2021. "Forecasting China’s CO2 emissions by considering interaction of bilateral FDI using the improved grey multivariable Verhulst model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 225-240, January.

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