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Robust Fuzzy Linear Regression And Application For Contact Identification


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Abstract

Linear regression has been used for a long time in various applications, with linear least squares as the best known tool. It is also known in probability  statistics theory that this method is particularly sensitive to outliers. Hence, statisticians introduced robust statistical tools that allow robustness and efficiency to overcome the effects of outlier. On the other hand, Tanaka and Hayashi provide basic ideas for fuzzy linear regression when data are rather ill-known and given in terms of fuzzy sets, even if the robustness of the method in the presence of outliers still is poor and over-estimated. This paper attempts to provide a hybrid approach by combining both robust statistical tools and the fuzzy approach. Particularly, the least median of squares estimator and the least trimmed squares estimator have been considered. The method is then tested in a robotics application where aforce-controlled contact situation is assessed.


Keywords


Pages

Total Pages: 9
Pages: 31-39

DOI
10.1080/10798587.2002.10644195


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Published

Volume: 8
Issue: 1
Year: 2002

Cite this document


References

D. Dubois and H. Prade, Fuzzy numbers: an overview, in: Analysis of Fuzzy Information, ed. J. Bezdek, CRS Press, pp.112–148, 1987.

M. Oussalah, H. Bruyninckx and J. De Schutter, “Contact Identification Using Fuzzy Linear Regression”, Proc. of IEEE International Conference on Intelligent Engineering Systems INES’99, Poprad 1–3 Nov, Slovakia, 643–648.

M. Oussalah, “Fuzzy Linear Regression for Contact Identification”, In: Proc. of the 2000 IEEE International Conference on Robotics and Automation, San Francisco (ICRA’2000), CA, USA, 2000, 3616–3621.

P. J. Rousseeuw and A M. Leroy, Robust Regression and Outliers Detections, John Wiley & Sons, 1987.

S. Siegel and N.J. Castellan, Nonparametric Statistics for the Behavioral Sciences, McGraw-Hill, New York, 1988.

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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