{"title":"多任务扩散最小平均四次算法","authors":"Qingyun Zhu","doi":"10.1109/ICEICT55736.2022.9909472","DOIUrl":null,"url":null,"abstract":"In some applications, the multitask network may be corrupted by non-Gaussian noise, e.g., uniform noise or binary noise. If the multitask diffusion LMS algorithm is used in such situations, its steady-state performance will be degraded. To overcome this issue, this work presents a multitask diffusion version of the least-mean-fourth algorithm by using the fourth-order moment of the estimation error. To further enhance its convergence rate, the $l_{0}$-norm regularization is used. Simulation results show that our algorithms can obtain small steady-state mean-square deviation (MSD).","PeriodicalId":179327,"journal":{"name":"2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multitask Diffusion Least-Mean-Fourth Algorithm\",\"authors\":\"Qingyun Zhu\",\"doi\":\"10.1109/ICEICT55736.2022.9909472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In some applications, the multitask network may be corrupted by non-Gaussian noise, e.g., uniform noise or binary noise. If the multitask diffusion LMS algorithm is used in such situations, its steady-state performance will be degraded. To overcome this issue, this work presents a multitask diffusion version of the least-mean-fourth algorithm by using the fourth-order moment of the estimation error. To further enhance its convergence rate, the $l_{0}$-norm regularization is used. Simulation results show that our algorithms can obtain small steady-state mean-square deviation (MSD).\",\"PeriodicalId\":179327,\"journal\":{\"name\":\"2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEICT55736.2022.9909472\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEICT55736.2022.9909472","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In some applications, the multitask network may be corrupted by non-Gaussian noise, e.g., uniform noise or binary noise. If the multitask diffusion LMS algorithm is used in such situations, its steady-state performance will be degraded. To overcome this issue, this work presents a multitask diffusion version of the least-mean-fourth algorithm by using the fourth-order moment of the estimation error. To further enhance its convergence rate, the $l_{0}$-norm regularization is used. Simulation results show that our algorithms can obtain small steady-state mean-square deviation (MSD).
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