Robust fuzzy linear regression and application for contact identification
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 a force-controlled contact situation is assessed.
contact identification robust regression fuzzy regression