Polynomial sparse adaptive algorithm. Issue 25 (1st December 2016) - Maheshwari, J.; George, N.V.
Abstract : Sparse learning algorithms for system identification differ from their non‐sparse counterparts in their improved ability in quickly identifying the zero coefficients in a sparse system. This improvement has been achieved using the principle of zero attraction, whereby the near zero coefficients of the model are forced to zero. In order to further improve the zero attraction capability of sparse adaptive algorithms, an attempt has been made to design a polynomial sparse adaptive algorithm. The enhanced modelling ability of the proposed scheme is evident from the simulation results. The proposed method has also been successfully applied in modelling an acoustic feedback path in a behind the ear digital hearing aid.