Research on Railway Engineering Cost Prediction Model Based on Chaotic Neural Networks and CS

Yan Chun Chen

doi:10.4028/www.scientific.net/AMR.204-210.1291

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 204-210Research on Railway Engineering Cost Prediction...

Research on Railway Engineering Cost Prediction Model Based on Chaotic Neural Networks and CS

Abstract:

It is always hard to draw on the experience of completed projects to predict engineering cost, and the nonlinear characteristic of the influence factors of engineering cost increases the difficulty of prediction. Less efforts and higher accuracy are the objects pursued by related researchers. In this paper, the Cost Significant theorem is applied to simplify computing and the chaotic neural network is used to improve accuracy. The prediction model is rooted from the nonlinear dynamic chaotic system theory and two techniques employed are phase space reconstruction and chaotic neural network construction. The experiment results indicate that the model is suitable for estimating short-term engineering investment and the prediction accuracy is improved.

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Periodical:

Advanced Materials Research (Volumes 204-210)

Pages:

1291-1294

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.204-210.1291

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Online since:

February 2011

Authors:

Yan Chun Chen

Keywords:

Chaotic Neural Network, Cost Significant, Engineering Cost, Prediction Model

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References

[1] Duan, X. C., Yu, J.X., Zhang, J.L. Railway Projects Based on CS, WLC and BPNN Theorems. Journal of the China Railway Society. Vol. 28-6(2006), pp.104-109.

Google Scholar

[2] Feng, G.Z. and Li, P.C. On the study of chaotic characteristics in hydrological systems. Acta Universitatis Agriculturae Boreali-occidentalis. Vol. 25-4(1997), pp.97-101.

Google Scholar

[3] Wang, H.R., Song, Y., and Liu, C.M. Application and issues of chaos theory in hydroscience. Advance in Water Science. Vol. 15(2004), pp.400-407.

Google Scholar

[4] Wang, Y.M., Xu, X.Y., and Yan, A.L. Stream flow forecasting based on the nearest adjacent. Systems Engineering. Vol. 26(2008), pp.111-115.

Google Scholar

[5] Liu, G., Huang, S. and Xu, M. Chaos analysis of the monthly runoff time series in Jinsha River. Journal of Chengdu University of Technology. Vol. 34(2007), pp.390-393.

Google Scholar

[6] Wang, Y. M., et al. Run-off prediction model based on the chaotic and BP network. Journal of Northwest A &F University. Vol. 38(2010), pp.200-204.

Google Scholar

[7] Yu, J. X., et al. Study on Forecasting Method of Construction Cost Based on Chaos and CS. Statistics and Decision. Vol. 14(2009), pp.19-21.

Google Scholar