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

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.