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Generalized kernel normalized mixed-norm algorithm: analysis and simulations
Yu, Shujian1; You, Xinge2; Jiang, Xiubao2; Ou, Weihua3; Zhu, Ziqi4; Zhao, Yixiao1; Chen, C L Philip5; Tang, Yuanyan2,5
2015
Conference Name22nd International Conference on Neural Information Processing, ICONIP 2015
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9490
Pages61-70
Conference Date11 9, 2015 - 11 12, 2015
Conference PlaceIstanbul, Turkey
Author of SourceSpringer Verlag
AbstractThis paper is a continuation and extension of our previ- ous research where kernel normalized mixed-norm (KNMN) algorithm, a combination of the kernel trick with the mixed-norm strategy, was proposed to demonstrate superior performance for system identification under non-Gaussian environment. Meanwhile, we also introduced a naive adaptive mixing parameter (AMP) updating mechanism to make KNMN more robust under nonstationary scenarios. The main contributions of this paper are threefold: firstly, the p-norm is substituted for the 4- norm in the cost function, which can be viewed as a generalized version to the form of mixed-norms; secondly, instead of using the original AMP proposed in our previous work, a novel time-varying AMP is employed to provide better tracking behavior to the nonstationarity; and thirdly, the mean square convergence analysis is conducted, where the second moment behavior of weight error vector is elaborately studied. Simulations are conducted on two benchmark system identification problems, and different kinds of additive noises are added respectively to verify the effectiveness of improvements. © Springer International Publishing Switzerland 2015.
DOI10.1007/978-3-319-26535-3_8
Language英语
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Department of Electrical and Computer Engineering, University of Florida, Gainesville; FL, United States;
2.School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan; Hubei, China;
3.School of Mathematics and Computer Science, Guizhou Normal University, Guiyang; Guizhou, China;
4.School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan; Hubei, China;
5.University of Macau, Macau, China
Recommended Citation
GB/T 7714
Yu, Shujian,You, Xinge,Jiang, Xiubao,et al. Generalized kernel normalized mixed-norm algorithm: analysis and simulations[C]//Springer Verlag,2015:61-70.
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