It is known that sign LMS and sign regressor LMS are faster than LMS. Inspiring from this idea we have proposed sign regressor Wilcoxon and sign-sign wilcoxon which are robust against the outlier present in the desired data and also faster than Wilcoxon and sign Wilcoxon norm. It had applied to varities of linear and nonlinear system identification problems with Gaussian noise and impulse noise present in the desired. The simulation results are compared among Wilcoxon, sign Wilcoxon and proposed sign-sign Wilcoxon and sign-regressor Wilcoxon. From simulation results it has proved that the proposed techniques are robust against outlier in the desired data
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