A sparse system identification algorithm based on fractional order LMS
by Yun Tan; Jiaohua Qin; Xuyu Xiang; Wentao Ma
International Journal of Embedded Systems (IJES), Vol. 13, No. 3, 2020

Abstract: In this paper, a zero-attracting fractional order least mean squares (ZA-FLMS) algorithm is proposed for adaptive sparse system identification. l1-norm of the filter coefficients is considered and zero-attracting correction is introduced into the updating equation. The extension of the algorithm is also proposed for distributed local sensors of information-centric IoT. The convergence speed and MSE performance are investigated in the simulations, which show effective improvement for sparse system identification compared with traditional LMS, zero-attracting LMS and fractional order LMS, especially with lower sparsity and smaller fractional order. But the decrement of fractional order and step size will lead to slower convergence speed, while the bigger fractional order and step size will lead to bigger variation of MSE. Therefore, optimised ZA-FLMS is further introduced, which uses l0-norm in the initial stage of the algorithm and shows improvement of convergence speed for smaller fractional order.

Online publication date: Wed, 30-Sep-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com