Abstract
As the phenomenon in the world is complicated, at the time of carrying on statistic forecast, we will usually meet a type of fuzzy number that points are constant, the circle is changed, and vice versa. For such a case, an analytical problem needs considering in regression and self-regression under a fuzzy environment. In 1980, in this aspect, a regression analysis formulation was already developed according to a possible linear system [TUA80]. Hereafter the regression analysis was variously formed by means of fuzzy data analysis, and carried in extensive application [TUA82]. In 1989, based on the theory of Zadeh fuzzy sets [Zad65a], self-regression forecast model with fuzzy coefficients was advanced [cao89b][cao90].
This chapter introduces a regression and self-regression model containing (·, c) fuzzy coefficients, flat fuzzy coefficients as well as triangular fuzzy coefficients, concludes the regression analysis as a linear programming.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cao, BY. (2010). Regression and Self-regression Models with Fuzzy Coefficients. In: Optimal Models and Methods with Fuzzy Quantities. Studies in Fuzziness and Soft Computing, vol 248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10712-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-10712-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10710-8
Online ISBN: 978-3-642-10712-2
eBook Packages: EngineeringEngineering (R0)