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- Title
A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production.
- Authors
Roldán, C.; Roldán, A.; Martínez-Moreno, J.
- Abstract
Least-squares technique is well-known and widely used to determine the coefficients of a explanatory model from observations based on a concept of distance. Traditionally, the observations consist of pairs of numeric values. However, in many real-life problems, the independent or explanatory variable can be observed precisely (for instance, the time) and the dependent or response variable is usually described by approximate values, such as 'about $$\pounds300$$' or 'approximately $500', instead of exact values, due to sources of uncertainty that may affect the response. In this paper, we present a new technique to obtain fuzzy regression models that consider triangular fuzzy numbers in the response variable. The procedure solves linear and non-linear problems and is easy to compute in practice and may be applied in different contexts. The usefulness of the proposed method is illustrated using simulated and real-life examples.
- Subjects
CASE studies; REGRESSION analysis; BIOMASS production; LEAST squares; FUZZY numbers
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2012, Vol 16, Issue 5, p785
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-011-0769-1