Analysis of outliers in system identification using WLMS algorithm

Outliers play an important role in adaptive systems. The rank-based Wilcoxon approach to linear regression problems in statistics are usually insensitive to outliers. This paper aim towards the Wilcoxon approach in Least Mean Square Algorithm. Also it has been applied for System Identification problem with Gaussian noise. The traditional LMS algorithm is generally well suited for identification of linear static systems where the probability of addition of outliers to data input is minimal. The investigation regarding the performance the performance analysis, error curve and deviation in presence of outliers are presented. Simulation results show that the Wilcoxon norm based LMS have better robustness against outliers.