Zero-attracted Lorentzian Algorithm for System Identification

In this paper, a zero attracted Lorentzian (ZAL) algorithm is created for system identification applications. The proposed ZAL algorithm is realized by using l ₁ -norm penalty on the Lorentzian-based cost function to exploit the sparseness of the existing nature signals. Moreover, a reweighting method is adopt to enhance the ability of the proposed ZAL algorithm, and the new algorithm is called as reweighting zero attracted Lorentzian (RZAL) algorithm. The proposed algorithms are presented briefly and investigated via computer simulations. The gotten results from the simulation demonstrate that the proposed algorithms are superior to the popular adaptive filtering algorithms under alpha-stable noise interference.