Abstract
In this work, the e-normalized sign regressor least mean mixed-norm (NSRLMMN) adaptive algorithm is proposed. The proposed algorithm exhibits increased convergence rate as compared to the least mean mixed-norm (LMMN) and the sign regressor least mean mixed-norm (SRLMMN) algorithms. Also, the steady-state analysis and convergence analysis are presented. Moreover, the proposed e-NSRLMMN algorithm substantially reduces the computational load, a major drawback of the e-normalized least mean mixed-norm (NLMMN) algorithm. Finally, simulation results are presented to support the theoretical findings.
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