A robust least squares fuzzy regression model based on kernel function

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Khammar A. H.; arefi M.; AKBARI M.G.

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Journal Paper

Paper Information

Journal: Iranian Journal of Fuzzy Systems Year:2020 | Volume:17 | Issue:4 Page(s): 105-119

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Information Journal Paper

Title

A robust least squares fuzzy regression model based on kernel function

Author(s)

Khammar A. H. | arefi M. | AKBARI M.G. | Issue Writer Certificate

Keywords

DistanceQ2

kernel functionQ2

least squares methodQ2

outliersQ2

robust fuzzy regressionQ2

Abstract

In this paper, a new approach is presented to fit a robust fuzzy regression model based on some fuzzy quantities. In this approach, we first introduce a new Distance between two fuzzy numbers using the kernel function, and then, based on the least squares method, the parameters of fuzzy regression model is estimated. The proposed approach has a suitable performance to present the robust fuzzy model in the presence of different types of outliers. Using some simulated data sets and some real data sets, the application of the proposed approach in modeling some characteristics with outliers, is studied.

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APA: Copy

Khammar, A. H., arefi, M., & AKBARI, M.G.. (2020). A robust least squares fuzzy regression model based on kernel function. IRANIAN JOURNAL OF FUZZY SYSTEMS, 17(4 ), 105-119. SID. https://sid.ir/paper/380410/en

Vancouver: Copy

Khammar A. H., arefi M., AKBARI M.G.. A robust least squares fuzzy regression model based on kernel function. IRANIAN JOURNAL OF FUZZY SYSTEMS[Internet]. 2020;17(4 ):105-119. Available from: https://sid.ir/paper/380410/en

IEEE: Copy

A. H. Khammar, M. arefi, and M.G. AKBARI, “A robust least squares fuzzy regression model based on kernel function,” IRANIAN JOURNAL OF FUZZY SYSTEMS, vol. 17, no. 4 , pp. 105–119, 2020, [Online]. Available: https://sid.ir/paper/380410/en

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