Abstract
The zero attracting least mean square algorithm has improved performance than conventional LMS when the system is sparse and its performance decreases when the sparsity level is decreased or when the system is time varying. The proposed algorithm focused on optimization of both step size and zero attractor controller using state variable model to improve the overall performance at all sparsity levels. Simulations in the context of time varying sparse system identification proved that the proposed algorithm provides good performance when compared to the conventional ones.
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