| Autor: |
Laura Jazmín Hidalgo Hernández, Ángel Alfonso Vázquez Piña, Xochitl Maya Rosales, Juan Gerardo Avalos Ochoa, Giovanny Sánchez Rivera |
| Jazyk: |
English<br />Spanish; Castilian<br />Portuguese |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Revista Elektrón, Vol 6, Iss 2, Pp 96-100 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2525-0159 |
| DOI: |
10.37537/rev.elektron.6.2.163.2022 |
| Popis: |
Adaptive filters are used in a wide variety of signal processing applications (e.g., acoustic echo cancellation, system identification, channel equalization, etc.). Adaptive algorithms are an essential part of adaptive filters since they update the filter coefficients to model the desired response. Therefore, adaptive algorithms must have low computational cost and high speed of convergence. In this paper, a new variant of the Normalized Least-Mean-Fourth (NLMF) algorithm based on set membership is presented, in addition, a method to automatically adjust the step size is presented. To evaluate its performance, the algorithm was simulated in system identification and acoustic echo cancellation applications. The results demonstrate that the proposed algorithm improves the convergence speed and exhibits low computational cost compared to the conventional NLMS/F algorithm. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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