Abstract
In 1989, based on the theory of Zadeh fuzzy sets [Zad65a], self-regression forecast model with T -fuzzy variables was advanced [Cao89b],[Cao89c],[Cao 90a], and, again in 1992, a linearizable non-linear regression model with T-fuzzy variables [Cao95c] was developed. The application appears vastly extensive because of much wider information in models.
1) Make use of a fuzzy distance, follow the classic regression analytical method with a beeline( or curve) imitation.
2) Ascertain the regression model with fuzzy variables under a cone and platform index. Because fuzzy regression analysis is an interval estimation, a kind of analytical methods become much useful.
This chapter introduces T -fuzzy variables, (·, c) fuzzy variables and flat (or trapezoidal) fuzzy variables into regression models, and builds more practical kinds of way to the model determination. Meanwhile, their application is discussed.
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Cao, BY. (2010). Regression and Self-regression Models with Fuzzy Variables. 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_3
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DOI: https://doi.org/10.1007/978-3-642-10712-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10710-8
Online ISBN: 978-3-642-10712-2
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