Software Effort Estimation and Stock Market Prediction Using Takagi-Sugeno Fuzzy Models

Author

Sheta, Alaa

Author_Institution

Electron. Res. Inst., Giza

fYear

0

fDate

0-0 0

Firstpage

171

Lastpage

178

Abstract

In this paper, we use Takagi-Sugeno (TS) technique to develop fuzzy models for two nonlinear processes. They are the software effort estimation for a NASA software projects and the prediction of the next week S&P 500 for stock market. The development of the TS fuzzy model can be achieved in two steps 1) the determination of the membership functions in the rule antecedents using the model input data; 2) the estimation of the consequence parameters. We use least-square estimation to estimate those parameters. Detailed descriptions of the two applications are given. The results are promising.

Keywords

fuzzy logic; fuzzy set theory; least squares approximations; software development management; stock markets; NASA software projects; Takagi-Sugeno fuzzy models; least-square estimation; membership functions; nonlinear processes; rule antecedents; software effort estimation; stock market prediction; Fuzzy logic; Fuzzy sets; Fuzzy systems; Nonlinear dynamical systems; Nonlinear systems; Parameter estimation; Predictive models; Stock markets; System identification; Takagi-Sugeno model;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2006 IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9488-7

Type

conf

DOI

10.1109/FUZZY.2006.1681711

Filename

1681711