A NEW VIEWPOINT OF S-CURVE REGRESSION MODEL AND ITS APPLICATION TO CONSTRUCTION MANAGEMENT

Author:

HSIEH TING-YA1,WANG MORRIS H. L.2,CHEN CHENG-WU3,CHEN CHEN-YUAN4,YU SHANG-EN5,YANG HSIEN-CHUEH6,CHEN TSUNG-HAO6

Affiliation:

1. Department of Civil Engineering, National Central University, Chung-li, Taiwan 320, R.O.C.

2. Department of Finance, Vanung University, 1 Vanung Rd., Chung-li, Taiwan 320, R.O.C.

3. Department of Logistics Management, Shu-Te University, 59 Hun Shan Rd., Yen Chau, Kaohsiung, Taiwan 82445, R.O.C.

4. Department of Marine Environment and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C.

5. Department of Finance, Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan, Taiwan 701, R.O.C.

6. Doctoral Program in Management, National Kaohsiung First University of Science and Technology, No. 2, Jhuoyue Rd., Nanzih District, Kaohsiung City 811, Taiwan, R.O.C.

Abstract

The least square method is in generally used for curve fitting problems. We here propose a fuzzy S-curve regression model to deal with the case in which the observed data are given by fuzzy numbers. The fuzzy regression curve, obtained for project control and predicting the progress of large-scale or small-scale engineering, is smoothly connected by a Takagi-Sugeno (T-S) fuzzy model. This paper also proposes the concept that the upper bound and lower bound are given instead of the confidence interval when the observed data are not obtained exactly. Based on the project cash flow and progress payment records of an example project taken from the Department of Rapid Transit Systems, Taipei City Government, this model is demonstrated and tentative conclusions concerning the model are given. The S-curve equation developed here could be used in a variety of applications related to project control for the management of working capital for construction firms.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

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