Research on Selecting Real Estate Project Based on PCA and RBF Neural Network

Hui Zhao; Li Ming Chen

doi:10.4028/www.scientific.net/AMR.225-226.162

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 225-226Research on Selecting Real Estate Project Based on...

Research on Selecting Real Estate Project Based on PCA and RBF Neural Network

Abstract:

A new method based on the integration of principal component analysis (PCA) and radial basic function (RBF) neural network is put forward for selecting the real estate project. Firstly, principal component analysis (PCA) is used to reduce the evaluation index dimensions. And then, radial basic function (RBF) neural network is used to evaluate the real estate projects. In order to grasp this method better, finally, the paper provides a case to demonstrate the application of this method in selecting the real estate project. The case has shown that the method applied to select the real estate project is feasible and reliable.

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Periodical:

Advanced Materials Research (Volumes 225-226)

Pages:

162-165

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.225-226.162

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Online since:

April 2011

Authors:

Hui Zhao, Li Ming Chen

Keywords:

Principal Component Analysis (PCA), RBF, Real Estate Project

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References

[1] WANG XQ, YU G. Study of construction project bidding based on the BP neural network improved by GA. China Civil Engineering Journal, vol. 40 (2007), pp.93-98.

Google Scholar

[2] HU SS, HE YQ. Study on rough sets theory and its application. Beijing: Beihang University press (2005).

Google Scholar

[3] MONTAZER GA, SABZEVARIR, KHATIRH GP. Intelligent parameter reduction using rough sets theory and sensitivity analysis. WSEAS Transaction on Systems, vol. 6 (2007), pp.623-630.

Google Scholar

[4] TAYF E H, SHEN L X. Fault diagnosis based on rough set theory. Engineering Applications of Artificial Intelligence, vol. 16 (2003), pp.39-43.

DOI: 10.1016/s0952-1976(03)00022-8

Google Scholar

[5] LIH, SHEN L Y, LOVE P ED. ANN-based mark-up estimation system with self-explanatory capacities. Journal of Construction Engineering and Management, vol. 125 (1999), pp.185-189.

DOI: 10.1061/(asce)0733-9364(1999)125:3(185)

Google Scholar

[6] M. Caudill and C. Butler. Understanding Neural Networks: Computer Explorations, Vol. 1 : Basic Networks (M1T Press, Cambridge, MA, 1992).

Google Scholar