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The q-gradient LMS spline adaptive filtering algorithm and its variable step-size variant.
- Source :
-
Information Sciences . Feb2024, Vol. 658, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- In this study, an innovation q-gradient least-mean-square (LMS) spline adaptive filtering (S-AF) algorithm (SAF-qLMS) on the basis of the theory of q-derivative is proposed. The q-calculus confronts the issue of slow convergence by mitigating the over-reliance of LMS-type algorithms on the diffusion of eigenvalues in the input correlation matrix. Compared to conventional derivatives, the SAF-qLMS exploits q-calculus to compute the secant of the cost function, enabling it to take larger steps in the search direction for q > 1. Furthermore, for balancing the convergence rate and steady-state error of SAF-qLMS and solving the deficiency of the fixed step-size, the SAF-VqLMS based on variable step-size is further proposed. Finally, the convergence conditions of the SAF-qLMS are discussed. Simulations in a correlated Gaussian input environment confirm the outstanding performance of the proposed algorithms for nonlinear system identification. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ADAPTIVE filters
*COST functions
*SPLINES
*SYSTEM identification
*NONLINEAR systems
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 658
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 174604907
- Full Text :
- https://doi.org/10.1016/j.ins.2023.119983