{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T09:47:06Z","timestamp":1784368026303,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62201097"],"award-info":[{"award-number":["62201097"]}]},{"name":"National Natural Science Foundation of China","award":["62303077"],"award-info":[{"award-number":["62303077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In active noise control (ANC) systems, the traditional filtered-x least mean square (FxLMS) algorithm has poor control effect on impulsive noise. To overcome this drawback, a robust cost function was designed in this paper by embedding the cost function of the FxLMS algorithm into the framework of hyperbolic tangent function; this paper thus proposes a robust filtered-x least hyperbolic tangent (FxLHT) algorithm in ANC systems. Moreover, the value of \u03bb in the FxLHT algorithm greatly affects the robustness and convergence performance of the algorithm. Therefore, a variable \u03bb-parameter was proposed to enhance the performance of the FxLHT algorithm. Simulation results show that in the active control of impulsive noise, compared with the FxLMS algorithm and other robust ANC algorithms, the proposed FxLHT algorithm and variable \u03bb-parameter FxLHT algorithm not only exhibit good robustness and noise reduction performance but also have a better tracking ability.<\/jats:p>","DOI":"10.3390\/sym16081031","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T08:54:08Z","timestamp":1723452848000},"page":"1031","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Robust Filtered-x Least Mean Square Algorithm with Adjustable Parameters for Active Impulsive Noise Control"],"prefix":"10.3390","volume":"16","author":[{"given":"Pucha","family":"Song","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.asoc.2016.01.051","article-title":"Improved functional link artificial neural network via convex combination for nonlinear active noise control","volume":"42","author":"Zhao","year":"2016","journal-title":"Appl. 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