Predicting Short-Term Orders by an Improved Grey Neural Network Model

Sheng Long Yang; Sheng Ma Zhang; Cui Hua Wang; Jun Jie Ma

doi:10.4028/www.scientific.net/AMM.321-324.2227

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Predicting Short-Term Orders by an Improved Grey...

Predicting Short-Term Orders by an Improved Grey Neural Network Model

Abstract:

According to the validation that the random selection of the gray neural network parameters random selection is similar to initial the space position of the particle in the particle swarm algorithm, the gray neural network based on the modified particle swarm optimization (PSO) algorithm is established to improve the robustness and the precision of the net model with applying a improved PSO algorithm to instead of gradient correction method, updating the network parameter and searching the best individual in this algorithm. There are several methods to forecast the short-term orders, including BP, the gray network, the original PSO algorithm and the improved PSO algorithm. Comparing with these methods, the results demonstrated the grey network based on the improved PSO algorithm has better approximation ability and prediction accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

2227-2231

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.2227

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Online since:

June 2013

Authors:

Sheng Long Yang, Sheng Ma Zhang, Cui Hua Wang, Jun Jie Ma

Keywords:

Grey Model, Neural Network Technology, Particle Swarm Optimization (PSO)

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References

[1] J L Deng. The primary methods of grey system theory.( Huazhong University of Science and Technology Press, Wuhan,2005)（In Chinese）

Google Scholar

[2] J L Yuan, L Zhong, X Y Li. The research and development of grey neural network, Journal of Wuhan University of Technology. 3(2009)91（In Chinese）．

Google Scholar

[3] R C Eberhart, J Kennedy.A new optimizer using particle swarm theory[C]// Proceedings of the Sixth International Symposium on Micromachine and Human Science.Nagoya: IEEE Press,1995:39-43．

DOI: 10.1109/mhs.1995.494215

Google Scholar

[4] Kennedy J，Eberhart R C．Particle swarm optimization[c]// Proceedings of IEEE International Conference on Neural Networks．Perth：IEEE Press,1995:1942-1948．

Google Scholar

[5] J Kennedy, R C Eberhart. Swarm intelligence. (Kaufmann, Morgan 2001).

Google Scholar

[6] R Poli, J Kennedy, T Blackwell. Particle swarm optimization an overview, Swarm Intelligence.1(2007)33-57.

DOI: 10.1007/s11721-007-0002-0

Google Scholar

[7] F.Shi, X.C. Wang, L.Yu, etc.30 instance analysis of matlab neural network.( Beihang University Press,Beijing 2010) (In Chinese)

Google Scholar