A Neural Network Forecasting Model of Beijing Motor Vehicles Sold Based on Set Pare Analysis

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Abstract:

By applying neural networks to forecasting Beijing motor vehicles sold, sequencing the principal factors and analyzing the development trend using connection number and partial connection number of the set pair analysis (SPA), we set up the forecasting model of Beijing motor vehicles sold. The instance analysis shows that it is a scientific and suitable system analyzing method of high forecasting accuracy.

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Periodical:

Advanced Materials Research (Volumes 403-408)

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2333-2336

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November 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Beijing Bureau of Statistics. Beijing Statistical Yearbook . Beijing: Statistics Press of China, 2000-(2010).

Google Scholar

[2] Wen Jiabao. Report on the work of the government. Journal of Beijing Review, 12(2010), p.53.

Google Scholar

[3] Zhao Keqin. Set Pair Analysis and Its Elementary Application . Science and Tech. Press of Zhejian(2000).

Google Scholar

[4] Zhang Xiaoxi. Control Theory of Mathematics and Application. Science and Tech. Press of Inner Mongol(1999).

Google Scholar

[5] Shen Dingzhu. The Application of SPA in Sports. Beijing: The Cultural and Educational Press (2007).

Google Scholar

[6] Shao Zhuyan. Set pair analysis model of forecast epidemic cerebrospinal meningitis incidence. Journal of Jining Medical College, Vol. 29(2006), pp.43-44.

Google Scholar

[7] Chen Yanqing. Neural Networks Theory and Its Applications in Control Engineering . Xian: Northwest Industry University Press, (1991).

Google Scholar

[8] Cao Hongxing. General System Perimeter Theory-Periphery Theory. Science and Document Press(1997).

Google Scholar

[9] Nguyen D.H., Widrow B. Neural Networks for Self-Learning Control Systems . IEEE Control Systems Magazine, Vol. 4(1990), pp.18-23.

DOI: 10.1109/37.55119

Google Scholar

[10] Hecht-Nielsen R. Theory of the Back Propagation Neural Networks. Proc. of IJCNN, Vol. 1(1989), pp.593-603.

Google Scholar