Nonnegative least mean mixed-norm algorithm: Analysis and performance

Sun, Zeyang; Li, Yingsong

doi:10.1016/j.dsp.2021.103215

Cited by 8 publications

(2 citation statements)

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“…In the following, we investigate the behavior of the NNMCC algorithm when 2(b). The NNLMF algorithm proposed in [26], the R-NNLMS algorithm presented in [27] and the NNLMMN algorithm presented in [28] This assumption is reasonable as the same matrix h(p) hT (p) can be obtained from infinitely many different vectors h(p).…”

Section: Simulations Of the Nnmcc Algorithmmentioning

confidence: 99%

“…Hence, it is important to efficient AF algorithms to identify systems under nonnegative constraints for operation in the presence of the non-Gaussian noise. The nonnegative least mean fourth (NNLMF) algorithm, the robust non-negative least mean square algorithm (R-NNLMS) and the nonnegative least mean mix-norm (NNLMMN) algorithm have been proposed to this end [26,27,28]. Nevertheless, the performance of the NNLMF algorithm may degrade in some certain non-Gaussian noise environments, such as heavy-tailed noises.…”

Section: Introductionmentioning

confidence: 99%

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Nonnegative Maximum Correntropy Adaptive Algorithm and Its Statistical Analysis

Sun¹,

Li²

2022

Preprint

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Dynamic system modeling methods have become a hot topic for stationary and nonstationary signal processing. Nonnegativity is a desired constraint that usually exerts on to be estimated parameters, and its generation usually based on the inherent physical characteristics of unknown system. Moreover, non-Gaussian noise is present in many practical system identification situations. In this paper, an adaptive nonnegative maximum correntropy criterion (NNMCC) algorithm is proposed for system identification under non-negativity constraints. We derive the NNMCC algorithm based on the Karush-Kuhn-Tucker conditions and a fixed-point iteration scheme. The first-order and second-order moments of the NNMCC algorithm adaptive weights are theoretically analyzed. Experimental results validate the theoretical analysis and illustrate the superior performance of NNMCC in non-Gaussian noise environments.

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Section: Simulations Of the Nnmcc Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%