{"title":"Multi-reference Adaptive Gain FXLMS Algorithm for Active Noise Control","authors":"Quanjiang Wu, Pengwei Wen, X. Chai, Hui Yang, Jiaxin Chen, Limin Zhang","doi":"10.1109/ICCCS57501.2023.10151068","DOIUrl":null,"url":null,"abstract":"Multi-reference least mean square algorithm (MR-FXLMS) has been performed more efficiently than the traditional Least mean square algorithm (FXLMS). In this paper, a multi-reference adaptive gain (MRAG-FXLMS) algorithm which is designed to enhance the performance of the multi-reference FXLMS algorithm. The proposed method is derived by combining a novel multi-reference structure and adaptive gain. The adaptive gain is obtained by using the gradient descent method. To evaluate the effectiveness of the proposed algorithm, a computational complexity analysis is conducted. According to the simulation results, it can be concluded that the proposed MRAG-FXLMS algorithm achieves excellent performance in convergence rate and steady-state error compared with the conventional FXLMS algorithm and MR-FXLMS algorithm.1","PeriodicalId":266168,"journal":{"name":"2023 8th International Conference on Computer and Communication Systems (ICCCS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 8th International Conference on Computer and Communication Systems (ICCCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCS57501.2023.10151068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-reference least mean square algorithm (MR-FXLMS) has been performed more efficiently than the traditional Least mean square algorithm (FXLMS). In this paper, a multi-reference adaptive gain (MRAG-FXLMS) algorithm which is designed to enhance the performance of the multi-reference FXLMS algorithm. The proposed method is derived by combining a novel multi-reference structure and adaptive gain. The adaptive gain is obtained by using the gradient descent method. To evaluate the effectiveness of the proposed algorithm, a computational complexity analysis is conducted. According to the simulation results, it can be concluded that the proposed MRAG-FXLMS algorithm achieves excellent performance in convergence rate and steady-state error compared with the conventional FXLMS algorithm and MR-FXLMS algorithm.1