Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms

Ahmad Pouradabi; Amir Rastegarnia; Azam Khalili; Ali Farzamnia

doi:10.1109/IICAIET55139.2022.9936845

Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms

Ahmad Pouradabi, Amir Rastegarnia, Azam Khalili, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In diffusion estimation of distributed networks two characteristic parameters are crucial, the speed of convergence and steady-state error. Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. On the contrary, the diffusion normalized subband adaptive filter (DNSAF) algorithm has faster convergence than DNLMS, but final steady-state error is higher. In this paper, the overall performance is improved by combining these algorithms. Convex combination of DNLMS / DNSAF has a quick convergence rate and little steady-state error. The introduced algorithms execute tracking more effectively than traditional algorithms, in addition. We use a number of experimental findings to show how well the suggested method performs.

Original language	English
Title of host publication	4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9781665468374
ISBN (Print)	9781665468381
DOIs	https://doi.org/10.1109/IICAIET55139.2022.9936845
Publication status	Published - 9 Nov 2022
Externally published	Yes
Event	4th IEEE International Conference on Artificial Intelligence in Engineering and Technology - Kota Kinabalu, Malaysia Duration: 13 Sept 2022 → 15 Sept 2022 Conference number: 4

Conference

Conference	4th IEEE International Conference on Artificial Intelligence in Engineering and Technology
Abbreviated title	IICAIET 2022
Country/Territory	Malaysia
City	Kota Kinabalu
Period	13/09/22 → 15/09/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/IICAIET55139.2022.9936845Licence: Unspecified

Cite this

Pouradabi, A., Rastegarnia, A., Khalili, A., & Farzamnia, A. (2022). Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms. In 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022 Article 9936845 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IICAIET55139.2022.9936845

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title = "Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms",

abstract = "In diffusion estimation of distributed networks two characteristic parameters are crucial, the speed of convergence and steady-state error. Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. On the contrary, the diffusion normalized subband adaptive filter (DNSAF) algorithm has faster convergence than DNLMS, but final steady-state error is higher. In this paper, the overall performance is improved by combining these algorithms. Convex combination of DNLMS / DNSAF has a quick convergence rate and little steady-state error. The introduced algorithms execute tracking more effectively than traditional algorithms, in addition. We use a number of experimental findings to show how well the suggested method performs.",

keywords = "convergence, convex combination, diffusion estimation, distributed networks, DNLMS, DNSAF, learning curve",

author = "Ahmad Pouradabi and Amir Rastegarnia and Azam Khalili and Ali Farzamnia",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022 ; Conference date: 13-09-2022 Through 15-09-2022",

year = "2022",

month = nov,

day = "9",

doi = "10.1109/IICAIET55139.2022.9936845",

language = "English",

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booktitle = "4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022",

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Pouradabi, A, Rastegarnia, A, Khalili, A & Farzamnia, A 2022, Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms. in 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022., 9936845, Institute of Electrical and Electronics Engineers Inc., 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, Kota Kinabalu, Malaysia, 13/09/22. https://doi.org/10.1109/IICAIET55139.2022.9936845

Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms. / Pouradabi, Ahmad; Rastegarnia, Amir; Khalili, Azam et al.
4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. 9936845.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms

AU - Pouradabi, Ahmad

AU - Rastegarnia, Amir

AU - Khalili, Azam

AU - Farzamnia, Ali

N1 - Conference code: 4

PY - 2022/11/9

Y1 - 2022/11/9

N2 - In diffusion estimation of distributed networks two characteristic parameters are crucial, the speed of convergence and steady-state error. Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. On the contrary, the diffusion normalized subband adaptive filter (DNSAF) algorithm has faster convergence than DNLMS, but final steady-state error is higher. In this paper, the overall performance is improved by combining these algorithms. Convex combination of DNLMS / DNSAF has a quick convergence rate and little steady-state error. The introduced algorithms execute tracking more effectively than traditional algorithms, in addition. We use a number of experimental findings to show how well the suggested method performs.

AB - In diffusion estimation of distributed networks two characteristic parameters are crucial, the speed of convergence and steady-state error. Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. On the contrary, the diffusion normalized subband adaptive filter (DNSAF) algorithm has faster convergence than DNLMS, but final steady-state error is higher. In this paper, the overall performance is improved by combining these algorithms. Convex combination of DNLMS / DNSAF has a quick convergence rate and little steady-state error. The introduced algorithms execute tracking more effectively than traditional algorithms, in addition. We use a number of experimental findings to show how well the suggested method performs.

KW - convergence

KW - convex combination

KW - diffusion estimation

KW - distributed networks

KW - DNLMS

KW - DNSAF

KW - learning curve

UR - https://www.scopus.com/pages/publications/85142260825

U2 - 10.1109/IICAIET55139.2022.9936845

DO - 10.1109/IICAIET55139.2022.9936845

M3 - Conference contribution

AN - SCOPUS:85142260825

SN - 9781665468381

BT - 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology

Y2 - 13 September 2022 through 15 September 2022

ER -

Improved Performance in Distributed Estimation by Convex Combination of DNSAF and DNLMS Algorithms

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