Nonlinear Active Noise Control using partial-update Filtered-s LMS algorithm

Kurczyk, Sebastian; Pawełczyk, Marek

doi:10.1109/mmar.2015.7283982

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2016

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such algorithms can reduce computational complexity and on the other hand perform close to the full-update methods in term of convergence rate and Mean Error Square (MSE). There are several variants of the LMS algorithms with partial update methods [3][4][5][6][7][8][9][10][11][12][13][14]. In addition, partial-update adaptive filters may suffer from stability or convergence problems when the input signal is cyclostationary or periodic [15].…”

Section: Introductionmentioning

confidence: 99%

A New Variable Length LMS Algorithm for Partial Update Adaptive Filtering Driven by Cyclostaionary Signal

Jamel¹,

Hammood²

2016

IJCDS

View full text Add to dashboard Cite

The problem with some methods of Partial Update (PU) is that when using a special kind of input (Cyclostationary Signal), the system fails to converge or becomes unstable. These problems have been solved in this paper by using a newly proposed algorithm with two concepts of dynamic length Least Mean Square (LMS). The first is the length variation of the total filter coefficients N , while the second variation applies to the number of coefficients to be updated at each iteration M. This algorithm is called New Variable Length LMS algorithm NVLLMS. In the NVLLMS algorithm, the value of the Mean Error Square (MSE) is used to control N and M. Moreover, the step size is time varying. The proposed algorithm shows better performance compared with PU LMS algorithms through simulation results of system identification .

show abstract