Hybrid fuzzy regression with trapezoidal fuzzy data

T. Razzaghnia; S. Danesh; A. Maleki

doi:10.1117/12.923172

13 January 2012 Hybrid fuzzy regression with trapezoidal fuzzy data

T. Razzaghnia, S. Danesh, A. Maleki

Proceedings Volume 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis; 834921 (2012) https://doi.org/10.1117/12.923172
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore

Abstract

In this regard, this research deals with a method for hybrid fuzzy least-squares regression. The extension of symmetric triangular fuzzy coefficients to asymmetric trapezoidal fuzzy coefficients is considered as an effective measure for removing unnecessary fuzziness of the linear fuzzy model. First, trapezoidal fuzzy variable is applied to derive a bivariate regression model. In the following, normal equations are formulated to solve the four parts of hybrid regression coefficients. Also the model is extended to multiple regression analysis. Eventually, method is compared with Y-H.O. chang's model.

Citation Download Citation

T. Razzaghnia, S. Danesh, and A. Maleki "Hybrid fuzzy regression with trapezoidal fuzzy data", Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 834921 (13 January 2012); https://doi.org/10.1117/12.923172

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