Convergence analysis of the mixed-norm LMS and two versions for sparse system identification

Eleyan, Gülden; Salman, Mohammad Shukri

doi:10.1007/s11760-019-01628-9

Cited by 3 publications

(1 citation statement)

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“…To enhance estimation for sparse channel estimation usage, sparse least-mean mixed-norm technique has been created [9,10]. Eleyan et al studied the convergence behavior of the MN-LMS algorithm, which performed significantly better than other algorithms at different sparsity and SNR [11]. Dogariu LM.…”

Section: Introductionmentioning

confidence: 99%

Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

Tajdari

2021

FACTA U EE

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This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

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Section: Introductionmentioning

confidence: 99%