Momentum fractional LMS for power signal parameter estimation

• Published in: Signal processing Vol. 142; pp. 441 - 449
• Main Authors: Zubair, Syed, Chaudhary, Naveed Ishtiaq, Khan, Zeshan Aslam, Wang, Wenwu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2018
• Subjects: Fractional algorithms; Parameter estimation; Power signal estimation; Signal processing; Signal processing; Parameter estimation; Fractional algorithms; Power signal estimation
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2017.08.009

•A new fractional calculus based LMS algorithm with fast convergence is proposed.•A new momentum term is included in fraction LMS algorithm for fast convergence.•Parameter estimation of a power signal using momentum fractional LMS algorithm.•Superiority of the proposed algorithm over LMS, momentum LMS and fractional LMS. [Display omitted] Fractional adaptive algorithms have given rise to new dimensions in parameter estimation of control and signal processing systems. In this paper, we present novel fractional calculus based LMS algorithm with fast convergence properties and potential ability to avoid being trapped into local minima. We test our proposed algorithm for parameter estimation of power signals and compare it with other state-of-the-art fractional and standard LMS algorithms under different noisy conditions. Our proposed algorithm outperforms other LMS algorithms in terms of convergence rate and accuracy.