Enhanced q-least Mean Square

Alishba Sadiq, Shujaat Khan, Imran Naseem, Roberto Togneri, Mohammed Bennamoun

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

In this work, a new class of stochastic gradient algorithm is developed based on q-calculus. Unlike the existing q-LMS algorithm, the proposed approach fully utilizes the concept of q-calculus by incorporating a time-varying q parameter. The proposed enhanced q-LMS (Eq-LMS) algorithm utilizes a novel, parameterless concept of error-correlation energy and normalization of signal to ensure high convergence, stability and low steady-state error. The proposed algorithm automatically adapts the learning rate with respect to the error. For evaluation purposes the system identification problem is considered. The necessary condition of convergence for the proposed algorithm is analyzed, and the validation of analytical findings and simulation results is discussed. Extensive experiments show better performance of the proposed Eq-LMS algorithm compared to the standard q-LMS approach.

Original languageEnglish
Pages (from-to)4817-4839
Number of pages23
JournalCircuits, Systems, and Signal Processing
Volume38
Issue number10
Early online date26 Mar 2019
DOIs
Publication statusPublished - 1 Oct 2019

Fingerprint

Dive into the research topics of 'Enhanced q-least Mean Square'. Together they form a unique fingerprint.

Cite this