An application of fuzzy linear regression to the information technology in Turkey

Fuzzy set theory deals with the vagueness of human thought. A major contribution of fuzzy set theory is its capability of representing vague knowledge. Fuzzy set theory is very practical when sufficient and reliable data isn't available. Information Technology (IT) is the acquisition, processing, storage and dissemination of information in all its forms (auditory, pictorial, textual and numerical) through a combination of computers, telecommunication, networks and electronic devices. IT includes matters concerned with the furtherance of computer science and technology, design, development, installation and implementation of information systems and applications. In the paper, assuming that there are n independent variables and the regression function is linear, the possible levels of information technology (the sale levels of computer equipment) in Turkey will be forecasted by using fuzzy linear regression. The independent variables assumed will be the import level and the export level of computer equipment.

[1]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[2]  Chui-Yu Chiu,et al.  FUZZY CASH FLOW ANALYSIS USING PRESENT WORTH CRITERION , 1994 .

[3]  A. Fiordaliso.  A nonlinear forecasts combination method based on Takagi–Sugeno fuzzy systems , 1998 .

[4]  L. Fan,et al.  Fuzzy linear regression analysis of cellulose hydrolysis , 1986 .

[5]  Gwo-Hshiung Tzeng,et al.  A general piecewise necessity regression analysis based on linear programming , 1999, Fuzzy Sets Syst..

[6]  J. Watada,et al.  Possibilistic linear systems and their application to the linear regression model , 1988 .

[7]  R. J. Kuo,et al.  A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights , 1998, Decis. Support Syst..

[8]  T. Lindström,et al.  A fuzzy design of the willingness to invest in Sweden , 1998 .

[9]  I. Burhan Turksen,et al.  A fuzzy set model for market share and preference prediction , 1995 .

[10]  Chao-Yen Wu,et al.  A decision support system for capital budgeting and allocation , 1990 .

[11]  Kwang Jae Kim,et al.  A comparison of fuzzy and nonparametric linear regression , 1997, Comput. Oper. Res..

[12]  Gwo-Hshiung Tzeng,et al.  Fuzzy Seasonal Time Series for Forecasting the Production Value of the Mechanical Industry in Taiwan , 1999 .

[13]  Hideo Tanaka,et al.  Upper and lower approximation models in interval regression using regression quantile techniques , 1999, Eur. J. Oper. Res..

[14]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[15]  H. Zimmermann,et al.  Fuzzy sets theory and applications , 1986 .

[16]  E. Tolga,et al.  Justification of manufacturing technologies using fuzzy benefit/cost ratio analysis , 2000 .

[17]  Yufei Yuan,et al.  Dealing with Fuzziness in Cost-Volume-Profit Analysis , 1990 .

[18]  Robert Gray,et al.  An intelligent business forecaster for strategic business planning , 1999 .

[19]  A. Kandel,et al.  Fuzzy linear regression and its applications to forecasting in uncertain environment , 1985 .

[20]  H. Moskowitz,et al.  Fuzzy versus statistical linear regression , 1996 .