Hybrid Least-Squares Regression Modelling Using Confidence Bounds

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)

Abstract

One of the questions regarding bridging of soft computing and statistical methods is the (re-)use of information between the two approaches. In this context, we consider in this paper whether statistical confidence bounds can be used in the hybrid fuzzy least squares regression problem. By using the confidence limits as the spreads of the fuzzy numbers, uncertainty estimates for the fuzzy model can be provided. Experiments have been conducted in the paper, both on regression coefficients and the predicted responses of regression models. The findings show that the use of the confidence intervals as the widths of memberships gives successful results and opens new possibilities in system modeling and analysis.

Keywords

Fuzzy Number Triangular Fuzzy Number Fuzzy Random Variable Fuzzy Regression Fuzzy Arithmetic 
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.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Engineeringİnönü UniversityMalatyaTurkey
  2. 2.School of Industrial EngineeringEindhoven University of TechnologyEindhovenThe Netherlands

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