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The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and n series related, abbreviated as GDMC(1,n), performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n), n interpolation coefficients (taken as unknown parameters) are introduced into the background values of the n 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(1,n) model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n) model. The modelling results can assist the government in developing future policies regarding high-tech industry management

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